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- <p>Free Fire Advance Server is a great opportunity for advanced players who want to experience new features and updates before they are released to the public. By joining Free Fire Advance Server, you will be able to access new weapons, characters, skins, modes, maps, events, and more before anyone else. You will also be able to provide your feedback and suggestions directly to the developers, which may influence the final version of the updates. Moreover, you will be rewarded with diamonds for finding and reporting bugs on the server.</p>
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-- <h2>Introduction</h2>
-  - What is The Baby In Yellow and what is it about?   - Why is it a horror game and what makes it scary?   - How can you download and play it on your Android device? - <h2>The Baby In Yellow: A Game That Will Test Your Nerves</h2>
-  - <h3>The premise and the gameplay of The Baby In Yellow</h3>
-    - You are a babysitter who has to take care of a baby in a yellow onesie     - The baby is not a normal baby, but a demonic entity that can do strange things     - You have to follow the instructions on the screen and try to survive the night   - <h3>The graphics and the sound effects of The Baby In Yellow</h3>
-    - The game has a low-poly style that creates a contrast between the cute and the creepy     - The game has a dark and eerie atmosphere that builds up tension and suspense     - The game has realistic and disturbing sound effects that add to the horror - <h2>How to Download and Play The Baby In Yellow on Your Android Device</h2>
-  - <h3>The requirements and the compatibility of The Baby In Yellow</h3>
-    - The game requires Android 4.4 or higher and 136 MB of free space     - The game is compatible with most Android devices, but some may experience performance issues     - The game is free to download and play, but it may contain ads or in-app purchases   - <h3>The steps to download and install The Baby In Yellow</h3>
-    - Go to one of the trusted sources that offer the APK file of The Baby In Yellow, such as [Softonic](^1^), [Tamindir](^2^), or [APKCombo](^3^)     - Tap on the download button and wait for the file to be downloaded     - Go to your device settings and enable the installation of apps from unknown sources     - Locate the downloaded file in your file manager and tap on it to install it     - Launch the game and enjoy the horror - <h2>Conclusion</h2>
-  - Summarize the main points of the article   - Restate the thesis and provide a call to action or an interesting insight - <h2>FAQs</h2>
-  - List five unique FAQs related to the topic of the article Article with HTML formatting: <h1>The Baby In Yellow: A Horror Game That Will Make You Think Twice About Babysitting</h1>
- <h2>Introduction</h2>
- <p>If you are looking for a horror game that will challenge your nerves and make you jump out of your seat, you might want to try The Baby In Yellow. This is a first-person horror game developed by Team Terrible, where you will simulate the life of a babysitter. However, what you will babysit is more sinister than he first appears. The Baby In Yellow follows the same premise as the PC game, but it is now available for Android devices. In this article, we will tell you what The Baby In Yellow is about, why it is a horror game, and how you can download and play it on your Android device.</p>
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- <h2>The Baby In Yellow: A Game That Will Test Your Nerves</h2>
- <h3>The premise and the gameplay of The Baby In Yellow</h3>
- <p>In The Baby In Yellow, you are a babysitter who has to take care of a baby in a yellow onesie. Sounds easy, right? Well, not quite. The baby is not a normal baby, but a demonic entity that can do strange things. He can teleport, levitate, laugh maniacally, stare at you with glowing eyes, and even summon fire. He can also escape from his crib, his room, or even his house. Your job is to follow the instructions on the screen and try to survive the night. You will have to feed him, change his diaper, put him to bed, and deal with his mischief. But be careful, because he might not like what you do.</p>
- <h3>The graphics and the sound effects of The Baby In Yellow</h3>
- from. The game has realistic and disturbing sound effects that add to the horror. You will hear the baby's cries, laughs, whispers, and screams, as well as the creaking of doors, the flickering of lights, and the thumping of footsteps.</p>
- <h2>How to Download and Play The Baby In Yellow on Your Android Device</h2>
- <h3>The requirements and the compatibility of The Baby In Yellow</h3>
- <p>The game requires Android 4.4 or higher and 136 MB of free space. The game is compatible with most Android devices, but some may experience performance issues. The game is free to download and play, but it may contain ads or in-app purchases.</p>
- <h3>The steps to download and install The Baby In Yellow</h3>
- <p>To download and play The Baby In Yellow on your Android device, you need to follow these steps:</p>
- <table>
-<tr>
-<th>Step</th>
-<th>Instruction</th>
-</tr>
-<tr>
-<td>1</td>
-<td>Go to one of the trusted sources that offer the APK file of The Baby In Yellow, such as <a href="^1^">Softonic</a>, <a href="^2^">Tamindir</a>, or <a href="^3^">APKCombo</a>.</td>
-</tr>
-<tr>
-<td>2</td>
-<td>Tap on the download button and wait for the file to be downloaded.</td>
-</tr>
-<tr>
-<td>3</td>
-<td>Go to your device settings and enable the installation of apps from unknown sources.</td>
-</tr>
-<tr>
-<td>4</td>
-<td>Locate the downloaded file in your file manager and tap on it to install it.</td>
-</tr>
-<tr>
-<td>5</td>
-<td>Launch the game and enjoy the horror.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
- <p>The Baby In Yellow is a horror game that will make you think twice about babysitting. It is a game that will test your nerves and make you jump out of your seat. It is a game that has a low-poly style, a dark and eerie atmosphere, and realistic and disturbing sound effects. It is a game that is available for Android devices and can be downloaded and played for free. If you are looking for a horror game that will challenge you and scare you, you might want to try The Baby In Yellow. But be warned, this is not a game for the faint-hearted.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions related to The Baby In Yellow:</p>
- <ol>
-<li><b>Is The Baby In Yellow based on a true story?</b></li>
-<p>No, The Baby In Yellow is not based on a true story. It is a fictional horror game inspired by a short film called <a href="^4^">The Thing in the Apartment Chapter 2</a>, which was directed by John William Ross.</p>
- <li><b>Is The Baby In Yellow safe to play?</b></li>
-<p>The Baby In Yellow is safe to play as long as you are aware that it is a horror game that contains scary and violent scenes. It is not recommended for children or people who are sensitive to horror or gore. It is also advisable to play it in a well-lit room and with someone else nearby.</p>
- <li><b>How long does it take to finish The Baby In Yellow?</b></li>
-<p>The Baby In Yellow is a short game that can be finished in about 15 minutes. However, it has multiple endings depending on your choices and actions. You can replay the game to see different outcomes and discover more secrets.</p>
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- <li><b>What are some tips and tricks to play The Baby In Yellow?</b></li>
-<p>Some tips and tricks to play The Baby In Yellow are:</p>
- <ul>
-<li>Pay attention to the instructions on the screen and follow them carefully.</li>
-<li>Use the flashlight to see better in the dark.</li>
-<li>Avoid looking at the baby's eyes or touching him when he is angry.</li>
-<li>Hide in the closet or under the bed if you hear something suspicious.</li>
-<li>Don't let the baby escape from his room or his house.</li>
-<li>Don't trust everything you see or hear.</li>
-</ul>
- <li><b>Where can I find more games like The Baby In Yellow?</b></li>
-<p>If you enjoyed playing The Baby In Yellow, you might also like these games:</p>
- <ul>
-<li><a href="^6^">Five Nights at Freddy's</a>: A horror game where you have to survive five nights in a pizzeria haunted by animatronic animals.</li>
-<li><a href="^7^">Slendrina: The Cellar</a>: A horror game where you have to explore a cellar and avoid a ghostly woman.</li>
-<li><a href="^8^">Eyes: The Horror Game</a>: A horror game where you have to collect valuables in a haunted house and avoid a monster.</li>
-<li><a href="^9^">Hello Neighbor</a>: A stealth horror game where you have to sneak into your neighbor's house and discover his secrets.</li>
-</ul>
-</ol>
- <p>I hope you enjoyed reading this article and learned something new. If you have any questions or comments, feel free to leave them below. And if you want to play The Baby In Yellow, don't forget to download it from one of the sources mentioned above. But be careful, because this game is not for the faint-hearted.</p> 401be4b1e0<br />
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-<h1>Epic War 6 APK: A Thrilling Battle Game for Android</h1>
-<p>If you are looking for a game that combines strategy, action, and fantasy, then you should check out <strong>Epic War 6 APK</strong>. This is a game that lets you command legendary heroes and a strong army in epic battles against powerful enemies. You can choose from six unique heroes, each with their own strengths, weaknesses, and skills. You can also train and upgrade over 40 battle units, from archers and knights to dragons and giants. You can also challenge and defeat huge titans that will test your skills and strategy. And if you want to compete with other players from around the world, you can enter the PVP Arena and show how epic you are.</p>
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-<p>In this article, we will tell you everything you need to know about Epic War 6 APK, including its features, how to download and install it, how to play it, how it compares with other games, what are its pros and cons, and what is its review. By the end of this article, you will have a clear idea of whether this game is worth playing or not.</p>
- <h2>Features of Epic War 6 APK</h2>
-<p>Epic War 6 APK has a lot of features that make it a fun and exciting game to play. Here are some of them:</p>
-<ul>
-<li><strong>6 unique heroes</strong>: You can choose from six different heroes, each with their own personality, backstory, and abilities. Some of them are based on famous characters from mythology or history, such as Thor, Hercules, or Joan of Arc. Each hero has a special skill that can change the outcome of the battle, such as summoning thunderstorms, healing allies, or boosting morale.</li <p><li><strong>Over 40 battle units</strong>: You can train and upgrade a variety of units to fight for you in the battlefield. You can choose from different classes, such as infantry, cavalry, ranged, magic, or special. Each class has its own advantages and disadvantages, and you need to balance your army composition according to the situation. You can also unlock new units as you progress in the game, such as ninjas, samurais, or angels.</li>
-<li><strong>10 powerful titans</strong>: You can face and defeat 10 massive titans that will pose a great challenge to your skills and strategy. These titans are based on mythical creatures, such as dragons, hydras, or krakens. They have different abilities and weaknesses, and you need to find the best way to exploit them. You can also use your hero's skill to deal extra damage or gain an edge in the fight.</li>
-<li><strong>PVP Arena</strong>: You can compete with other players from around the world in the PVP Arena mode. You can choose your hero and units and enter a random match against another player. You can also join a clan and participate in clan wars, where you can cooperate with your clan members and fight against other clans. You can earn rewards and rank up in the leaderboards by winning matches and wars.</li>
-</ul>
- <h2>How to Download and Install Epic War 6 APK</h2>
-<p>If you want to play Epic War 6 APK on your Android device, you need to download and install it first. Here are the steps that you need to follow:</p>
-<ol>
-<li><strong>Go to the official website of mob.org</strong>: This is one of the best sources for downloading free Android games. You can access it by typing <a href="">mob.org</a> in your browser or clicking on this link. </li>
-<li><strong>Search for Epic War 6 APK</strong>: Once you are on the website, you can use the search bar to look for Epic War 6 APK. You can also browse through the categories or genres to find it. Alternatively, you can use this direct link to go to the download page of Epic War 6 APK.</li>
-<li><strong>Click on the download button</strong>: When you find the game that you want, you can click on the green download button that says "Download Epic War 6". This will start the download process and you will see a progress bar on your screen.</li>
-<li><strong>Enable unknown sources on your device settings</strong>: Before you can install the APK file that you downloaded, you need to allow your device to install apps from unknown sources. To do this, go to your device settings and look for security or privacy options. Then, find the option that says "Unknown sources" or "Allow installation of apps from unknown sources" and enable it.</li>
-<li><strong>Install the APK file</strong>: After enabling unknown sources, you can go to your file manager or downloads folder and find the APK file that you downloaded. Tap on it and follow the instructions on your screen to install it.</li>
-<li><strong>Launch the game and enjoy the epic battles</strong>: Once the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer. You can then start playing the game and enjoy the epic battles.</li>
-</ol> <h2>Gameplay Tips and Tricks for Epic War 6 APK</h2>
-<p>Epic War 6 APK is a game that requires strategy, skill, and patience. You need to plan your moves carefully and use your resources wisely. Here are some tips and tricks that can help you improve your gameplay and win more battles:</p>
-<ul>
-<li><strong>Choose your hero wisely</strong>: Each hero has a different skill that can affect the battle in various ways. For example, Thor can summon thunderstorms that deal damage to all enemies, Hercules can heal all allies and boost their morale, and Joan of Arc can increase the attack and defense of all units. You need to choose the hero that suits your playstyle and strategy, and use their skill at the right time and place.</li>
-<li><strong>Use spells and skills at the right time and place</strong>: Apart from your hero's skill, you can also use spells that you can buy from the shop or earn from quests. These spells can have different effects, such as healing, damaging, freezing, or stunning. You need to use them wisely and strategically, as they have a cooldown time and a limited number of uses. You also need to aim them well, as some of them have a specific target or area of effect.</li>
-<li><strong>Upgrade your units and heroes regularly</strong>: As you progress in the game, you will face stronger enemies and tougher challenges. You need to upgrade your units and heroes regularly to increase their power and performance. You can upgrade them by using gold and gems that you can earn from battles, quests, or achievements. You can also equip them with items that you can buy from the shop or find in chests. These items can enhance their stats or give them special abilities.</li>
-<li><strong>Experiment with different combinations of units and heroes</strong>: There are many possible combinations of units and heroes that you can use in the game. You can mix and match different classes, such as infantry, cavalry, ranged, magic, or special. You can also try different heroes with different skills and abilities. You need to experiment with different combinations to find the best synergy and balance for your army.</li>
-</ul>
- <h2>Comparison of Epic War 6 APK with Other Games</h2>
-<p>Epic War 6 APK is not the only game that offers strategy and action in a fantasy setting. There are many other games that have similar or different features and gameplay. Here are some of them and how they compare with Epic War 6 APK:</p>
-<table>
-<tr>
-<th>Game</th>
-<th>Similarities</th>
-<th>Differences</th>
-</tr>
-<tr>
-<td>Epic War Saga</td>
-<td>- Same developer as Epic War 6 APK<br>- Similar gameplay but with more RPG elements<br>- Same genre of strategy and action</td>
-<td>- Fewer heroes, units, and titans than Epic War 6 APK<br>- More quests, missions, and achievements than Epic War 6 APK<br>- Different graphics style and theme than Epic War 6 APK</td>
-</tr>
-<tr>
-<td>Kingdom Rush</td>
-<td>- Same genre of strategy and action<br>- Similar gameplay but with tower defense elements<br>- Same theme of fantasy and mythology</td>
-<td>- Different developer than Epic War 6 APK<br>- Fewer heroes and units than Epic War 6 APK<br>- No titans or PVP mode in Kingdom Rush</td>
-</tr>
-<tr>
-<td>Clash of Clans</td>
-<td>- Same genre of strategy and action<br>- Similar gameplay but with base building and army management elements<br>- Same theme of fantasy and mythology</td>
-<td>- Different developer than Epic War 6 APK<br>- More online multiplayer features than Epic War 6 APK<br>- Different graphics style and tone than Epic War 6 APK</td>
-</tr>
-</table> <h2>Pros and Cons of Epic War 6 APK</h2>
-<p>Epic War 6 APK is a game that has many positive and negative aspects. Here are some of them:</p>
-<h3>Pros</h3>
-<ul>
-<li><strong>High-quality graphics</strong>: The game has impressive graphics that create a realistic and immersive experience. The heroes, units, and titans are well-designed and animated. The backgrounds and environments are detailed and colorful. The effects and sounds are also realistic and captivating.</li>
-<li><strong>Addictive gameplay</strong>: The game has a simple but engaging gameplay that keeps you hooked for hours. The battles are fast-paced and thrilling, with a lot of strategy and action involved. The game also has a lot of content and features to explore, such as quests, achievements, items, and PVP mode.</li>
-<li><strong>Diverse heroes and units</strong>: The game has a lot of variety and diversity in terms of heroes and units. You can choose from six different heroes, each with their own skills and abilities. You can also train and upgrade over 40 battle units, from archers and knights to dragons and giants. You can also unlock new units as you progress in the game, such as ninjas, samurais, or angels.</li>
-<li><strong>Online PVP mode</strong>: The game has an online PVP mode that lets you compete with other players from around the world. You can choose your hero and units and enter a random match against another player. You can also join a clan and participate in clan wars, where you can cooperate with your clan members and fight against other clans. You can earn rewards and rank up in the leaderboards by winning matches and wars.</li>
-<li><strong>Free to play</strong>: The game is free to download and play on your Android device. You do not need to pay anything to enjoy the game. You can also play the game offline without an internet connection.</li>
-</ul>
-<h3>Cons</h3>
-<ul>
-<li><strong>High learning curve</strong>: The game is not very easy to learn or master. You need to understand the mechanics and strategies of the game, such as how to use your hero's skill, how to upgrade your units, how to use spells, how to defeat titans, etc. You also need to practice a lot to improve your skills and performance.</li>
-<li><strong>Requires internet connection</strong>: The game requires an internet connection to access some of its features, such as PVP mode, clan wars, quests, achievements, etc. If you do not have a stable or fast internet connection, you may experience lagging or crashing issues.</li>
-<li><strong>May have bugs and glitches</strong>: The game may have some bugs and glitches that can affect your gameplay or experience. For example, some users have reported that the game freezes or crashes randomly, that the game does not save their progress or data, that the game does not load properly, etc.</li>
-<li><strong>May consume battery and storage space</strong>: The game may consume a lot of battery power and storage space on your device. This is because the game has high-quality graphics, sounds, and effects that require a lot of resources. You may need to charge your device frequently or clear some space on your device to play the game smoothly.</li>
-</ul>
- <h2>Review of Epic War 6 APK</h2>
-<p>Epic War 6 APK is a game that deserves a positive review from us. We think that it is a great game for fans of strategy and action games, with a lot of content and features to enjoy. We like the graphics, the gameplay, the diversity, and the online mode of the game. We think that it is a fun and exciting game to play.</p>
-<p>However, we also acknowledge that the game has some flaws that need to be fixed or improved. We think that the game is not very easy to learn or master, that it requires an internet connection for some features, that it may have some bugs and glitches, and that it may consume a lot of battery power and storage space on your device.</p>
-<p>Therefore, we give Epic War 6 APK a rating of 4.5 out of 5 stars based on our experience and feedback from other users. We think that it is a game worth playing if you like strategy and action games.</p>
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- <h2>Conclusion</h2>
-<p>In conclusion, Epic War 6 APK is a thrilling battle game for Android devices that lets you command legendary heroes and a strong army in epic battles against powerful enemies. You can choose from six unique heroes, each with their own skills and abilities. You can also train and upgrade over 40 battle units, from archers and knights to dragons and giants. You can also challenge and defeat huge titans that will test your skills and strategy. And if you want to compete with other players from around the world, you can enter the PVP Arena and show how epic you are.</p>
-<p>We have also told you how to download and install Epic War 6 APK on your device, how to play it, how it compares with other games, what are its pros and cons, and what is its review. We hope that this article has been helpful and informative for you.</p>
-<p>If you are interested in playing Epic War 6 APK, you can download it from the official website of mob.org or use this direct link. You can also visit the official Facebook page of the game for more updates and news. You can also watch this video for a preview of the game.</p>
-<p>Thank you for reading this article and we hope that you enjoy playing Epic War 6 APK. Have fun and good luck!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Epic War 6 APK:</p>
-<ol>
-<li><strong>What are the requirements to play Epic War 6 APK?</strong><br>
-You need an Android device with Android 4.1 or higher and at least 100 MB of free storage space to play Epic War 6 APK. You also need an internet connection to access some features of the game, such as PVP mode, clan wars, quests, achievements, etc.</li>
-<li><strong>Is Epic War 6 APK safe to download and install?</strong><br>
-Yes, Epic War 6 APK is safe to download and install on your device. It does not contain any viruses, malware, or spyware that can harm your device or data. However, you need to make sure that you download it from a trusted source, such as mob.org or the direct link that we provided in this article.</li>
-<li><strong>How can I get more gold and gems in Epic War 6 APK?</strong><br>
-You can get more gold and gems in Epic War 6 APK by winning battles, completing quests, achieving goals, opening chests, watching ads, or buying them with real money. You can use gold and gems to upgrade your units and heroes, buy items and spells, or unlock new features and content.</li>
-<li><strong>How can I join or create a clan in Epic War 6 APK?</strong><br>
-You can join or create a clan in Epic War 6 APK by going to the clan menu in the game. You can either search for an existing clan that suits your preferences and apply to join it, or create your own clan by choosing a name, a logo, and a description. You can also invite your friends or other players to join your clan. You can participate in clan wars, chat with your clan members, and share resources and tips with them.</li>
-<li><strong>How can I contact the developer of Epic War 6 APK?</strong><br>
-You can contact the developer of Epic War 6 APK by sending an email to [email protected] or by visiting their website at www.artlogicgames.com. You can also follow them on Facebook at www.facebook.com/epicwargames. You can send them your feedback, suggestions, questions, or complaints about the game.</li>
-</ol></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1toTree/lora_test/ppdiffusers/pipelines/latent_diffusion_uncond/__init__.py

@@ -1,17 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 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.
-
-# flake8: noqa
-from .pipeline_latent_diffusion_uncond import LDMPipeline

spaces/AIFILMS/generate_human_motion/app.py

@@ -1,319 +0,0 @@
-import sys
-import os
-import OpenGL.GL as gl
-os.environ["PYOPENGL_PLATFORM"] = "egl"
-os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
-os.system('pip install /home/user/app/pyrender')
-
-sys.argv = ['VQ-Trans/GPT_eval_multi.py']
-os.chdir('VQ-Trans')
-
-sys.path.append('/home/user/app/VQ-Trans')
-sys.path.append('/home/user/app/pyrender')
-
-import options.option_transformer as option_trans
-from huggingface_hub import snapshot_download
-model_path = snapshot_download(repo_id="vumichien/T2M-GPT")
-
-args = option_trans.get_args_parser()
-
-args.dataname = 't2m'
-args.resume_pth = f'{model_path}/VQVAE/net_last.pth'
-args.resume_trans = f'{model_path}/VQTransformer_corruption05/net_best_fid.pth'
-args.down_t = 2
-args.depth = 3
-args.block_size = 51
-
-import clip
-import torch
-import numpy as np
-import models.vqvae as vqvae
-import models.t2m_trans as trans
-from utils.motion_process import recover_from_ric
-import visualization.plot_3d_global as plot_3d
-from models.rotation2xyz import Rotation2xyz
-import numpy as np
-from trimesh import Trimesh
-import gc
-
-import torch
-from visualize.simplify_loc2rot import joints2smpl
-import pyrender
-# import matplotlib.pyplot as plt
-
-import io
-import imageio
-from shapely import geometry
-import trimesh
-from pyrender.constants import RenderFlags
-import math
-# import ffmpeg
-# from PIL import Image
-import hashlib
-import gradio as gr
-import moviepy.editor as mp
-
-## load clip model and datasets
-is_cuda = torch.cuda.is_available()
-device = torch.device("cuda" if is_cuda else "cpu")
-print(device)
-clip_model, clip_preprocess = clip.load("ViT-B/32", device=device, jit=False, download_root='./')  # Must set jit=False for training
-
-if is_cuda:
-    clip.model.convert_weights(clip_model)
-    
-clip_model.eval()
-for p in clip_model.parameters():
-    p.requires_grad = False
-
-net = vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
-                       args.nb_code,
-                       args.code_dim,
-                       args.output_emb_width,
-                       args.down_t,
-                       args.stride_t,
-                       args.width,
-                       args.depth,
-                       args.dilation_growth_rate)
-
-
-trans_encoder = trans.Text2Motion_Transformer(num_vq=args.nb_code, 
-                                embed_dim=1024, 
-                                clip_dim=args.clip_dim, 
-                                block_size=args.block_size, 
-                                num_layers=9, 
-                                n_head=16, 
-                                drop_out_rate=args.drop_out_rate, 
-                                fc_rate=args.ff_rate)
-
-
-print('loading checkpoint from {}'.format(args.resume_pth))
-ckpt = torch.load(args.resume_pth, map_location='cpu')
-net.load_state_dict(ckpt['net'], strict=True)
-net.eval()
-    
-print('loading transformer checkpoint from {}'.format(args.resume_trans))
-ckpt = torch.load(args.resume_trans, map_location='cpu')
-trans_encoder.load_state_dict(ckpt['trans'], strict=True)
-trans_encoder.eval()
-
-mean = torch.from_numpy(np.load(f'{model_path}/meta/mean.npy'))
-std = torch.from_numpy(np.load(f'{model_path}/meta/std.npy'))
-
-if is_cuda:
-    net.cuda()
-    trans_encoder.cuda()
-    mean = mean.cuda()
-    std = std.cuda()
-
-def render(motions, device_id=0, name='test_vis'):
-    frames, njoints, nfeats = motions.shape
-    MINS = motions.min(axis=0).min(axis=0)
-    MAXS = motions.max(axis=0).max(axis=0)
-
-    height_offset = MINS[1]
-    motions[:, :, 1] -= height_offset
-    trajec = motions[:, 0, [0, 2]]
-    is_cuda = torch.cuda.is_available()
-    # device = torch.device("cuda" if is_cuda else "cpu")
-    j2s = joints2smpl(num_frames=frames, device_id=0, cuda=is_cuda)
-    rot2xyz = Rotation2xyz(device=device)
-    faces = rot2xyz.smpl_model.faces
-
-    if not os.path.exists(f'output/{name}_pred.pt'): 
-        print(f'Running SMPLify, it may take a few minutes.')
-        motion_tensor, opt_dict = j2s.joint2smpl(motions)  # [nframes, njoints, 3]
-
-        vertices = rot2xyz(torch.tensor(motion_tensor).clone(), mask=None,
-                                        pose_rep='rot6d', translation=True, glob=True,
-                                        jointstype='vertices',
-                                        vertstrans=True)
-        vertices = vertices.detach().cpu()
-        torch.save(vertices, f'output/{name}_pred.pt')
-    else:
-        vertices = torch.load(f'output/{name}_pred.pt')
-    frames = vertices.shape[3] # shape: 1, nb_frames, 3, nb_joints
-    print(vertices.shape)
-    MINS = torch.min(torch.min(vertices[0], axis=0)[0], axis=1)[0]
-    MAXS = torch.max(torch.max(vertices[0], axis=0)[0], axis=1)[0]
-
-    out_list = []
-    
-    minx = MINS[0] - 0.5
-    maxx = MAXS[0] + 0.5
-    minz = MINS[2] - 0.5 
-    maxz = MAXS[2] + 0.5
-    polygon = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz], [maxx, minz]])
-    polygon_mesh = trimesh.creation.extrude_polygon(polygon, 1e-5)
-
-    vid = []
-    for i in range(frames):
-        if i % 10 == 0:
-            print(i)
-
-        mesh = Trimesh(vertices=vertices[0, :, :, i].squeeze().tolist(), faces=faces)
-
-        base_color = (0.11, 0.53, 0.8, 0.5)
-        ## OPAQUE rendering without alpha
-        ## BLEND rendering consider alpha 
-        material = pyrender.MetallicRoughnessMaterial(
-            metallicFactor=0.7,
-            alphaMode='OPAQUE',
-            baseColorFactor=base_color
-        )
-
-
-        mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
-
-        polygon_mesh.visual.face_colors = [0, 0, 0, 0.21]
-        polygon_render = pyrender.Mesh.from_trimesh(polygon_mesh, smooth=False)
-
-        bg_color = [1, 1, 1, 0.8]
-        scene = pyrender.Scene(bg_color=bg_color, ambient_light=(0.4, 0.4, 0.4))
-        
-        sx, sy, tx, ty = [0.75, 0.75, 0, 0.10]
-
-        camera = pyrender.PerspectiveCamera(yfov=(np.pi / 3.0))
-
-        light = pyrender.DirectionalLight(color=[1,1,1], intensity=300)
-
-        scene.add(mesh)
-
-        c = np.pi / 2
-
-        scene.add(polygon_render, pose=np.array([[ 1, 0, 0, 0],
-
-        [ 0, np.cos(c), -np.sin(c), MINS[1].cpu().numpy()],
-
-        [ 0, np.sin(c), np.cos(c), 0],
-
-        [ 0, 0, 0, 1]]))
-
-        light_pose = np.eye(4)
-        light_pose[:3, 3] = [0, -1, 1]
-        scene.add(light, pose=light_pose.copy())
-
-        light_pose[:3, 3] = [0, 1, 1]
-        scene.add(light, pose=light_pose.copy())
-
-        light_pose[:3, 3] = [1, 1, 2]
-        scene.add(light, pose=light_pose.copy())
-
-
-        c = -np.pi / 6
-
-        scene.add(camera, pose=[[ 1, 0, 0, (minx+maxx).cpu().numpy()/2],
-
-                                [ 0, np.cos(c), -np.sin(c), 1.5],
-
-                                [ 0, np.sin(c), np.cos(c), max(4, minz.cpu().numpy()+(1.5-MINS[1].cpu().numpy())*2, (maxx-minx).cpu().numpy())],
-
-                                [ 0, 0, 0, 1]
-                                ])
-        
-        # render scene
-        r = pyrender.OffscreenRenderer(960, 960)
-
-        color, _ = r.render(scene, flags=RenderFlags.RGBA)
-        # Image.fromarray(color).save(outdir+'/'+name+'_'+str(i)+'.png')
-
-        vid.append(color)
-
-        r.delete()
-
-    out = np.stack(vid, axis=0)
-    imageio.mimwrite(f'output/results.gif', out, fps=20)
-    out_video = mp.VideoFileClip(f'output/results.gif')
-    out_video.write_videofile("output/results.mp4")
-    del out, vertices
-    return f'output/results.mp4'
-
-def predict(clip_text, method='fast'):
-    gc.collect()
-    if torch.cuda.is_available():
-        text = clip.tokenize([clip_text], truncate=True).cuda()
-    else:
-        text = clip.tokenize([clip_text], truncate=True)
-    feat_clip_text = clip_model.encode_text(text).float()
-    index_motion = trans_encoder.sample(feat_clip_text[0:1], False)
-    pred_pose = net.forward_decoder(index_motion)
-    pred_xyz = recover_from_ric((pred_pose*std+mean).float(), 22)
-    output_name = hashlib.md5(clip_text.encode()).hexdigest()
-    if method == 'fast':
-        xyz = pred_xyz.reshape(1, -1, 22, 3)
-        pose_vis = plot_3d.draw_to_batch(xyz.detach().cpu().numpy(), title_batch=None, outname=[f'output/results.gif'])
-        out_video = mp.VideoFileClip("output/results.gif")
-        out_video.write_videofile("output/results.mp4")
-        return f'output/results.mp4'
-    elif method == 'slow':
-        output_path = render(pred_xyz.detach().cpu().numpy().squeeze(axis=0), device_id=0, name=output_name)
-        return output_path
-
-        
-# ---- Gradio Layout -----
-text_prompt = gr.Textbox(label="Text prompt", lines=1, interactive=True)
-video_out = gr.Video(label="Motion", mirror_webcam=False, interactive=False) 
-demo = gr.Blocks()
-demo.encrypt = False
-
-with demo:
-    gr.Markdown('''
-            <div>
-            <h1 style='text-align: center'>Generating Human Motion from Textual Descriptions (T2M-GPT)</h1>
-            This space uses <a href='https://mael-zys.github.io/T2M-GPT/' target='_blank'><b>T2M-GPT models</b></a> based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions🤗
-            </div>
-        ''')
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            <figure>
-              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow1.gif" alt="Demo Slow", width="425", height=480/>
-              <figcaption> a man starts off in an up right position with botg arms extended out by his sides, he then brings his arms down to his body and claps his hands together. after this he wals down amd the the left where he proceeds to sit on a seat
-              </figcaption>
-            </figure>
-            ''')
-        with gr.Column():
-            gr.Markdown('''
-            <figure>
-              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow2.gif" alt="Demo Slow 2", width="425", height=480/>
-              <figcaption> a person puts their hands together, leans forwards slightly then swings the arms from right to left
-              </figcaption>
-            </figure>
-            ''')
-        with gr.Column():
-            gr.Markdown('''
-            <figure>
-              <img src="https://huggingface.co/vumichien/T2M-GPT/resolve/main/demo_slow3.gif" alt="Demo Slow 3", width="425", height=480/>
-              <figcaption> a man is practicing the waltz with a partner
-              </figcaption>
-            </figure>
-            ''')
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown('''
-            ### Generate human motion by **T2M-GPT**
-            ##### Step 1. Give prompt text describing human motion
-            ##### Step 2. Choice method to render output (Fast: Sketch skeleton; Slow: SMPL mesh, only work with GPU and running time around 2 mins)
-            ##### Step 3. Generate output and enjoy
-            ''')
-    with gr.Column():
-        with gr.Row():
-            text_prompt.render()
-            method = gr.Dropdown(["slow", "fast"], label="Method", value="slow")
-        with gr.Row():
-            generate_btn = gr.Button("Generate")
-            generate_btn.click(predict, [text_prompt, method], [video_out], api_name="generate")
-            print(video_out)
-        with gr.Row():  
-            video_out.render()
-    with gr.Row(): 
-        gr.Markdown('''
-            ### You can test by following examples:
-            ''')
-    examples = gr.Examples(examples=
-            [ "a person jogs in place, slowly at first, then increases speed. they then back up and squat down.", 
-              "a man steps forward and does a handstand", 
-              "a man rises from the ground, walks in a circle and sits back down on the ground"],
-          label="Examples", inputs=[text_prompt])
-        
-demo.launch(debug=True)

spaces/AIZerotoHero-Health4All/02-ClinicalTerminology/README.md

@@ -1,12 +0,0 @@
----
-title: 02 ClinicalTerminology
-emoji: 🐠
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ASJMO/freegpt/client/css/buttons.css

@@ -1,4 +0,0 @@
-.buttons {
-	display: flex;
-	justify-content: left;
-}

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/GptGod.py

@@ -1,51 +0,0 @@
-from __future__ import annotations
-import secrets, json
-from aiohttp import ClientSession
-from typing import AsyncGenerator
-from .base_provider import AsyncGeneratorProvider
-from .helper import format_prompt
-
-class GptGod(AsyncGeneratorProvider):
-    url = "https://gptgod.site"
-    supports_gpt_35_turbo = True
-    working = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        **kwargs
-    ) -> AsyncGenerator:
-        headers = {
-            "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/118.0",
-            "Accept": "text/event-stream",
-            "Accept-Language": "de,en-US;q=0.7,en;q=0.3",
-            "Accept-Encoding": "gzip, deflate, br",
-            "Alt-Used": "gptgod.site",
-            "Connection": "keep-alive",
-            "Referer": "https://gptgod.site/",
-            "Sec-Fetch-Dest": "empty",
-            "Sec-Fetch-Mode": "cors",
-            "Sec-Fetch-Site": "same-origin",
-            "Pragma": "no-cache",
-            "Cache-Control": "no-cache",
-        }
-        async with ClientSession(headers=headers) as session:
-            prompt = format_prompt(messages)
-            data = {
-                "content": prompt,
-                "id": secrets.token_hex(16).zfill(32)
-            }
-            async with session.get(f"{cls.url}/api/session/free/gpt3p5", params=data) as response:
-                response.raise_for_status()
-                event = None
-                async for line in response.content:
-                    if line.startswith(b'event: '):
-                        event = line[7:-1]
-                    elif event == b"data" and line.startswith(b"data: "):
-                        data = json.loads(line[6:-1])
-                        if data:
-                            yield data
-                    elif event == b"done":
-                        break

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

@@ -1,79 +0,0 @@
-import CreateImage from './CreateImage.js';
-import CreateSprite from './CreateSprite.js';
-import CreateVideo from './CreateVideo.js';
-import CreateText from './CreateText.js';
-import CreateBBCodeText from './CreateBBCodeText.js';
-import CreateRoundRectangle from './CreateRoundRectangle.js';
-import CreateNinePatch from './CreateNinePatch.js';
-import CreateNinePatch2 from './CreateNinePatch2.js';
-import CreateCanvas from './CreateCanvas.js';
-import CreateCircleMaskImage from './CreateCircleMaskImage.js';
-import CreateSpace from './CreateSpace.js';
-
-import CreateSizer from './CreateSizer.js';
-import CreateFixWidthSizer from './CreateFixWidthSizer.js';
-import CreateGridSizer from './CreateGridSizer.js';
-import CreateOverlapSizer from './CreateOverlapSizer.js';
-
-import CreateButtons from './CreateButtons.js';
-import CreateFixWidthButtons from './CreateFixWidthButtons.js';
-import CreateGridButtons from './CreateGridButtons.js';
-
-import CreateLabel from './CreateLabel.js';
-import CreateBadgeLabel from './CreateBadgeLabel.js';
-import CreateDialog from './CreateDialog.js';
-import CreateTextBox from './CreateTextBox.js';
-import CreateSlider from './CreateSlider.js';
-import CreateNumberBar from './CreateNumberBar.js';
-import CreateScrollBar from './CreateScrollBar.js';
-import CreateTextArea from './CreateTextArea.js';
-import CreatePages from './CreatePages.js';
-import CreateToast from './CreateToast.js';
-import CreateKnob from './CreateKnob.js';
-import CreateHolyGrail from './CreateHolyGrail.js';
-import CreateMenu from './CreateMenu.js';
-
-var Builders = {
-    Image: CreateImage,
-    Sprite: CreateSprite,
-    Video: CreateVideo,
-    Text: CreateText,
-    BBCodeText: CreateBBCodeText,
-    RoundRectangle: CreateRoundRectangle,
-    Ninepatch: CreateNinePatch,
-    Ninepatch2: CreateNinePatch2,
-    Canvas: CreateCanvas,
-    CircleMaskImage: CreateCircleMaskImage,
-    Space: CreateSpace,
-
-    Sizer: CreateSizer,
-    FixWidthSizer: CreateFixWidthSizer,
-    GridSizer: CreateGridSizer,
-    OverlapSizer: CreateOverlapSizer,
-
-    Buttons: CreateButtons,
-    FixWidthButtons: CreateFixWidthButtons,
-    GridButtons: CreateGridButtons,
-
-    Label: CreateLabel,
-    BadgeLabel: CreateBadgeLabel,
-    Dialog: CreateDialog,
-    TextBox: CreateTextBox,
-    Slider: CreateSlider,
-    NumberBar: CreateNumberBar,
-    ScrollBar: CreateScrollBar,
-    TextArea: CreateTextArea,
-    Pages: CreatePages,
-    Toast: CreateToast,
-    Knob: CreateKnob,
-    HolyGrail: CreateHolyGrail,
-    Menu: CreateMenu,
-};
-
-/*
-function(scene, data, view, styles, customBuilders) {
-    return gameObject;
-}
-*/
-
-export default Builders;

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

@@ -1,8 +0,0 @@
-import CreateAnyLabel from './utils/CreateAnyLabel.js';
-import Label from '../../label/Label.js';
-
-var CreateLabel = function (scene, data, view, styles, customBuilders) {
-    return CreateAnyLabel(scene, data, view, styles, customBuilders, Label);
-}
-
-export default CreateLabel;

spaces/Allie7/Nose/Dockerfile

@@ -1,21 +0,0 @@
-FROM node: 18-bullseye-slim
-
-RUN apt-get update && \
-
-apt-get install -y git
-
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git/app
-
-WORKDIR /app
-
-RUN npm install
-
-COPY Dockerfile greeting.md env* ./
-
-RUN npm run build
-
-EXPOSE 7860
-
-ENV NODE_ENV=production
-
-CMD ["npm", "start" ]

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/model/__init__.py

@@ -1,6 +0,0 @@
-from src.model.styleRF import StyleRF
-from src.utils.registry import Registry
-
-MODEL_REGISTRY = Registry("MODEL")
-
-MODEL_REGISTRY.register(StyleRF)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/stable_diffusion_tensorrt_img2img.py

@@ -1,1055 +0,0 @@
-#
-# Copyright 2023 The HuggingFace Inc. team.
-# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# 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.
-
-import gc
-import os
-from collections import OrderedDict
-from copy import copy
-from typing import List, Optional, Union
-
-import numpy as np
-import onnx
-import onnx_graphsurgeon as gs
-import PIL
-import tensorrt as trt
-import torch
-from huggingface_hub import snapshot_download
-from onnx import shape_inference
-from polygraphy import cuda
-from polygraphy.backend.common import bytes_from_path
-from polygraphy.backend.onnx.loader import fold_constants
-from polygraphy.backend.trt import (
-    CreateConfig,
-    Profile,
-    engine_from_bytes,
-    engine_from_network,
-    network_from_onnx_path,
-    save_engine,
-)
-from polygraphy.backend.trt import util as trt_util
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion import (
-    StableDiffusionImg2ImgPipeline,
-    StableDiffusionPipelineOutput,
-    StableDiffusionSafetyChecker,
-)
-from diffusers.schedulers import DDIMScheduler
-from diffusers.utils import DIFFUSERS_CACHE, logging
-
-
-"""
-Installation instructions
-python3 -m pip install --upgrade transformers diffusers>=0.16.0
-python3 -m pip install --upgrade tensorrt>=8.6.1
-python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com
-python3 -m pip install onnxruntime
-"""
-
-TRT_LOGGER = trt.Logger(trt.Logger.ERROR)
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-# Map of numpy dtype -> torch dtype
-numpy_to_torch_dtype_dict = {
-    np.uint8: torch.uint8,
-    np.int8: torch.int8,
-    np.int16: torch.int16,
-    np.int32: torch.int32,
-    np.int64: torch.int64,
-    np.float16: torch.float16,
-    np.float32: torch.float32,
-    np.float64: torch.float64,
-    np.complex64: torch.complex64,
-    np.complex128: torch.complex128,
-}
-if np.version.full_version >= "1.24.0":
-    numpy_to_torch_dtype_dict[np.bool_] = torch.bool
-else:
-    numpy_to_torch_dtype_dict[np.bool] = torch.bool
-
-# Map of torch dtype -> numpy dtype
-torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()}
-
-
-def device_view(t):
-    return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype])
-
-
-def preprocess_image(image):
-    """
-    image: torch.Tensor
-    """
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h))
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image).contiguous()
-    return 2.0 * image - 1.0
-
-
-class Engine:
-    def __init__(self, engine_path):
-        self.engine_path = engine_path
-        self.engine = None
-        self.context = None
-        self.buffers = OrderedDict()
-        self.tensors = OrderedDict()
-
-    def __del__(self):
-        [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)]
-        del self.engine
-        del self.context
-        del self.buffers
-        del self.tensors
-
-    def build(
-        self,
-        onnx_path,
-        fp16,
-        input_profile=None,
-        enable_preview=False,
-        enable_all_tactics=False,
-        timing_cache=None,
-        workspace_size=0,
-    ):
-        logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}")
-        p = Profile()
-        if input_profile:
-            for name, dims in input_profile.items():
-                assert len(dims) == 3
-                p.add(name, min=dims[0], opt=dims[1], max=dims[2])
-
-        config_kwargs = {}
-
-        config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805]
-        if enable_preview:
-            # Faster dynamic shapes made optional since it increases engine build time.
-            config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805)
-        if workspace_size > 0:
-            config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size}
-        if not enable_all_tactics:
-            config_kwargs["tactic_sources"] = []
-
-        engine = engine_from_network(
-            network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]),
-            config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs),
-            save_timing_cache=timing_cache,
-        )
-        save_engine(engine, path=self.engine_path)
-
-    def load(self):
-        logger.warning(f"Loading TensorRT engine: {self.engine_path}")
-        self.engine = engine_from_bytes(bytes_from_path(self.engine_path))
-
-    def activate(self):
-        self.context = self.engine.create_execution_context()
-
-    def allocate_buffers(self, shape_dict=None, device="cuda"):
-        for idx in range(trt_util.get_bindings_per_profile(self.engine)):
-            binding = self.engine[idx]
-            if shape_dict and binding in shape_dict:
-                shape = shape_dict[binding]
-            else:
-                shape = self.engine.get_binding_shape(binding)
-            dtype = trt.nptype(self.engine.get_binding_dtype(binding))
-            if self.engine.binding_is_input(binding):
-                self.context.set_binding_shape(idx, shape)
-            tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device)
-            self.tensors[binding] = tensor
-            self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype)
-
-    def infer(self, feed_dict, stream):
-        start_binding, end_binding = trt_util.get_active_profile_bindings(self.context)
-        # shallow copy of ordered dict
-        device_buffers = copy(self.buffers)
-        for name, buf in feed_dict.items():
-            assert isinstance(buf, cuda.DeviceView)
-            device_buffers[name] = buf
-        bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()]
-        noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr)
-        if not noerror:
-            raise ValueError("ERROR: inference failed.")
-
-        return self.tensors
-
-
-class Optimizer:
-    def __init__(self, onnx_graph):
-        self.graph = gs.import_onnx(onnx_graph)
-
-    def cleanup(self, return_onnx=False):
-        self.graph.cleanup().toposort()
-        if return_onnx:
-            return gs.export_onnx(self.graph)
-
-    def select_outputs(self, keep, names=None):
-        self.graph.outputs = [self.graph.outputs[o] for o in keep]
-        if names:
-            for i, name in enumerate(names):
-                self.graph.outputs[i].name = name
-
-    def fold_constants(self, return_onnx=False):
-        onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
-        self.graph = gs.import_onnx(onnx_graph)
-        if return_onnx:
-            return onnx_graph
-
-    def infer_shapes(self, return_onnx=False):
-        onnx_graph = gs.export_onnx(self.graph)
-        if onnx_graph.ByteSize() > 2147483648:
-            raise TypeError("ERROR: model size exceeds supported 2GB limit")
-        else:
-            onnx_graph = shape_inference.infer_shapes(onnx_graph)
-
-        self.graph = gs.import_onnx(onnx_graph)
-        if return_onnx:
-            return onnx_graph
-
-
-class BaseModel:
-    def __init__(self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77):
-        self.model = model
-        self.name = "SD Model"
-        self.fp16 = fp16
-        self.device = device
-
-        self.min_batch = 1
-        self.max_batch = max_batch_size
-        self.min_image_shape = 256  # min image resolution: 256x256
-        self.max_image_shape = 1024  # max image resolution: 1024x1024
-        self.min_latent_shape = self.min_image_shape // 8
-        self.max_latent_shape = self.max_image_shape // 8
-
-        self.embedding_dim = embedding_dim
-        self.text_maxlen = text_maxlen
-
-    def get_model(self):
-        return self.model
-
-    def get_input_names(self):
-        pass
-
-    def get_output_names(self):
-        pass
-
-    def get_dynamic_axes(self):
-        return None
-
-    def get_sample_input(self, batch_size, image_height, image_width):
-        pass
-
-    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
-        return None
-
-    def get_shape_dict(self, batch_size, image_height, image_width):
-        return None
-
-    def optimize(self, onnx_graph):
-        opt = Optimizer(onnx_graph)
-        opt.cleanup()
-        opt.fold_constants()
-        opt.infer_shapes()
-        onnx_opt_graph = opt.cleanup(return_onnx=True)
-        return onnx_opt_graph
-
-    def check_dims(self, batch_size, image_height, image_width):
-        assert batch_size >= self.min_batch and batch_size <= self.max_batch
-        assert image_height % 8 == 0 or image_width % 8 == 0
-        latent_height = image_height // 8
-        latent_width = image_width // 8
-        assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape
-        assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape
-        return (latent_height, latent_width)
-
-    def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape):
-        min_batch = batch_size if static_batch else self.min_batch
-        max_batch = batch_size if static_batch else self.max_batch
-        latent_height = image_height // 8
-        latent_width = image_width // 8
-        min_image_height = image_height if static_shape else self.min_image_shape
-        max_image_height = image_height if static_shape else self.max_image_shape
-        min_image_width = image_width if static_shape else self.min_image_shape
-        max_image_width = image_width if static_shape else self.max_image_shape
-        min_latent_height = latent_height if static_shape else self.min_latent_shape
-        max_latent_height = latent_height if static_shape else self.max_latent_shape
-        min_latent_width = latent_width if static_shape else self.min_latent_shape
-        max_latent_width = latent_width if static_shape else self.max_latent_shape
-        return (
-            min_batch,
-            max_batch,
-            min_image_height,
-            max_image_height,
-            min_image_width,
-            max_image_width,
-            min_latent_height,
-            max_latent_height,
-            min_latent_width,
-            max_latent_width,
-        )
-
-
-def getOnnxPath(model_name, onnx_dir, opt=True):
-    return os.path.join(onnx_dir, model_name + (".opt" if opt else "") + ".onnx")
-
-
-def getEnginePath(model_name, engine_dir):
-    return os.path.join(engine_dir, model_name + ".plan")
-
-
-def build_engines(
-    models: dict,
-    engine_dir,
-    onnx_dir,
-    onnx_opset,
-    opt_image_height,
-    opt_image_width,
-    opt_batch_size=1,
-    force_engine_rebuild=False,
-    static_batch=False,
-    static_shape=True,
-    enable_preview=False,
-    enable_all_tactics=False,
-    timing_cache=None,
-    max_workspace_size=0,
-):
-    built_engines = {}
-    if not os.path.isdir(onnx_dir):
-        os.makedirs(onnx_dir)
-    if not os.path.isdir(engine_dir):
-        os.makedirs(engine_dir)
-
-    # Export models to ONNX
-    for model_name, model_obj in models.items():
-        engine_path = getEnginePath(model_name, engine_dir)
-        if force_engine_rebuild or not os.path.exists(engine_path):
-            logger.warning("Building Engines...")
-            logger.warning("Engine build can take a while to complete")
-            onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
-            onnx_opt_path = getOnnxPath(model_name, onnx_dir)
-            if force_engine_rebuild or not os.path.exists(onnx_opt_path):
-                if force_engine_rebuild or not os.path.exists(onnx_path):
-                    logger.warning(f"Exporting model: {onnx_path}")
-                    model = model_obj.get_model()
-                    with torch.inference_mode(), torch.autocast("cuda"):
-                        inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width)
-                        torch.onnx.export(
-                            model,
-                            inputs,
-                            onnx_path,
-                            export_params=True,
-                            opset_version=onnx_opset,
-                            do_constant_folding=True,
-                            input_names=model_obj.get_input_names(),
-                            output_names=model_obj.get_output_names(),
-                            dynamic_axes=model_obj.get_dynamic_axes(),
-                        )
-                    del model
-                    torch.cuda.empty_cache()
-                    gc.collect()
-                else:
-                    logger.warning(f"Found cached model: {onnx_path}")
-
-                # Optimize onnx
-                if force_engine_rebuild or not os.path.exists(onnx_opt_path):
-                    logger.warning(f"Generating optimizing model: {onnx_opt_path}")
-                    onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path))
-                    onnx.save(onnx_opt_graph, onnx_opt_path)
-                else:
-                    logger.warning(f"Found cached optimized model: {onnx_opt_path} ")
-
-    # Build TensorRT engines
-    for model_name, model_obj in models.items():
-        engine_path = getEnginePath(model_name, engine_dir)
-        engine = Engine(engine_path)
-        onnx_path = getOnnxPath(model_name, onnx_dir, opt=False)
-        onnx_opt_path = getOnnxPath(model_name, onnx_dir)
-
-        if force_engine_rebuild or not os.path.exists(engine.engine_path):
-            engine.build(
-                onnx_opt_path,
-                fp16=True,
-                input_profile=model_obj.get_input_profile(
-                    opt_batch_size,
-                    opt_image_height,
-                    opt_image_width,
-                    static_batch=static_batch,
-                    static_shape=static_shape,
-                ),
-                enable_preview=enable_preview,
-                timing_cache=timing_cache,
-                workspace_size=max_workspace_size,
-            )
-        built_engines[model_name] = engine
-
-    # Load and activate TensorRT engines
-    for model_name, model_obj in models.items():
-        engine = built_engines[model_name]
-        engine.load()
-        engine.activate()
-
-    return built_engines
-
-
-def runEngine(engine, feed_dict, stream):
-    return engine.infer(feed_dict, stream)
-
-
-class CLIP(BaseModel):
-    def __init__(self, model, device, max_batch_size, embedding_dim):
-        super(CLIP, self).__init__(
-            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
-        )
-        self.name = "CLIP"
-
-    def get_input_names(self):
-        return ["input_ids"]
-
-    def get_output_names(self):
-        return ["text_embeddings", "pooler_output"]
-
-    def get_dynamic_axes(self):
-        return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}}
-
-    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
-        self.check_dims(batch_size, image_height, image_width)
-        min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims(
-            batch_size, image_height, image_width, static_batch, static_shape
-        )
-        return {
-            "input_ids": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)]
-        }
-
-    def get_shape_dict(self, batch_size, image_height, image_width):
-        self.check_dims(batch_size, image_height, image_width)
-        return {
-            "input_ids": (batch_size, self.text_maxlen),
-            "text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim),
-        }
-
-    def get_sample_input(self, batch_size, image_height, image_width):
-        self.check_dims(batch_size, image_height, image_width)
-        return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device)
-
-    def optimize(self, onnx_graph):
-        opt = Optimizer(onnx_graph)
-        opt.select_outputs([0])  # delete graph output#1
-        opt.cleanup()
-        opt.fold_constants()
-        opt.infer_shapes()
-        opt.select_outputs([0], names=["text_embeddings"])  # rename network output
-        opt_onnx_graph = opt.cleanup(return_onnx=True)
-        return opt_onnx_graph
-
-
-def make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False):
-    return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
-
-
-class UNet(BaseModel):
-    def __init__(
-        self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4
-    ):
-        super(UNet, self).__init__(
-            model=model,
-            fp16=fp16,
-            device=device,
-            max_batch_size=max_batch_size,
-            embedding_dim=embedding_dim,
-            text_maxlen=text_maxlen,
-        )
-        self.unet_dim = unet_dim
-        self.name = "UNet"
-
-    def get_input_names(self):
-        return ["sample", "timestep", "encoder_hidden_states"]
-
-    def get_output_names(self):
-        return ["latent"]
-
-    def get_dynamic_axes(self):
-        return {
-            "sample": {0: "2B", 2: "H", 3: "W"},
-            "encoder_hidden_states": {0: "2B"},
-            "latent": {0: "2B", 2: "H", 3: "W"},
-        }
-
-    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        (
-            min_batch,
-            max_batch,
-            _,
-            _,
-            _,
-            _,
-            min_latent_height,
-            max_latent_height,
-            min_latent_width,
-            max_latent_width,
-        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
-        return {
-            "sample": [
-                (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width),
-                (2 * batch_size, self.unet_dim, latent_height, latent_width),
-                (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width),
-            ],
-            "encoder_hidden_states": [
-                (2 * min_batch, self.text_maxlen, self.embedding_dim),
-                (2 * batch_size, self.text_maxlen, self.embedding_dim),
-                (2 * max_batch, self.text_maxlen, self.embedding_dim),
-            ],
-        }
-
-    def get_shape_dict(self, batch_size, image_height, image_width):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        return {
-            "sample": (2 * batch_size, self.unet_dim, latent_height, latent_width),
-            "encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim),
-            "latent": (2 * batch_size, 4, latent_height, latent_width),
-        }
-
-    def get_sample_input(self, batch_size, image_height, image_width):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        dtype = torch.float16 if self.fp16 else torch.float32
-        return (
-            torch.randn(
-                2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device
-            ),
-            torch.tensor([1.0], dtype=torch.float32, device=self.device),
-            torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device),
-        )
-
-
-def make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False):
-    return UNet(
-        model,
-        fp16=True,
-        device=device,
-        max_batch_size=max_batch_size,
-        embedding_dim=embedding_dim,
-        unet_dim=(9 if inpaint else 4),
-    )
-
-
-class VAE(BaseModel):
-    def __init__(self, model, device, max_batch_size, embedding_dim):
-        super(VAE, self).__init__(
-            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
-        )
-        self.name = "VAE decoder"
-
-    def get_input_names(self):
-        return ["latent"]
-
-    def get_output_names(self):
-        return ["images"]
-
-    def get_dynamic_axes(self):
-        return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}}
-
-    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        (
-            min_batch,
-            max_batch,
-            _,
-            _,
-            _,
-            _,
-            min_latent_height,
-            max_latent_height,
-            min_latent_width,
-            max_latent_width,
-        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
-        return {
-            "latent": [
-                (min_batch, 4, min_latent_height, min_latent_width),
-                (batch_size, 4, latent_height, latent_width),
-                (max_batch, 4, max_latent_height, max_latent_width),
-            ]
-        }
-
-    def get_shape_dict(self, batch_size, image_height, image_width):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        return {
-            "latent": (batch_size, 4, latent_height, latent_width),
-            "images": (batch_size, 3, image_height, image_width),
-        }
-
-    def get_sample_input(self, batch_size, image_height, image_width):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device)
-
-
-def make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False):
-    return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
-
-
-class TorchVAEEncoder(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.vae_encoder = model
-
-    def forward(self, x):
-        return self.vae_encoder.encode(x).latent_dist.sample()
-
-
-class VAEEncoder(BaseModel):
-    def __init__(self, model, device, max_batch_size, embedding_dim):
-        super(VAEEncoder, self).__init__(
-            model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim
-        )
-        self.name = "VAE encoder"
-
-    def get_model(self):
-        vae_encoder = TorchVAEEncoder(self.model)
-        return vae_encoder
-
-    def get_input_names(self):
-        return ["images"]
-
-    def get_output_names(self):
-        return ["latent"]
-
-    def get_dynamic_axes(self):
-        return {"images": {0: "B", 2: "8H", 3: "8W"}, "latent": {0: "B", 2: "H", 3: "W"}}
-
-    def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape):
-        assert batch_size >= self.min_batch and batch_size <= self.max_batch
-        min_batch = batch_size if static_batch else self.min_batch
-        max_batch = batch_size if static_batch else self.max_batch
-        self.check_dims(batch_size, image_height, image_width)
-        (
-            min_batch,
-            max_batch,
-            min_image_height,
-            max_image_height,
-            min_image_width,
-            max_image_width,
-            _,
-            _,
-            _,
-            _,
-        ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape)
-
-        return {
-            "images": [
-                (min_batch, 3, min_image_height, min_image_width),
-                (batch_size, 3, image_height, image_width),
-                (max_batch, 3, max_image_height, max_image_width),
-            ]
-        }
-
-    def get_shape_dict(self, batch_size, image_height, image_width):
-        latent_height, latent_width = self.check_dims(batch_size, image_height, image_width)
-        return {
-            "images": (batch_size, 3, image_height, image_width),
-            "latent": (batch_size, 4, latent_height, latent_width),
-        }
-
-    def get_sample_input(self, batch_size, image_height, image_width):
-        self.check_dims(batch_size, image_height, image_width)
-        return torch.randn(batch_size, 3, image_height, image_width, dtype=torch.float32, device=self.device)
-
-
-def make_VAEEncoder(model, device, max_batch_size, embedding_dim, inpaint=False):
-    return VAEEncoder(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim)
-
-
-class TensorRTStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
-    r"""
-    Pipeline for image-to-image generation using TensorRT accelerated Stable Diffusion.
-
-    This model inherits from [`StableDiffusionImg2ImgPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`StableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPFeatureExtractor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: DDIMScheduler,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
-        requires_safety_checker: bool = True,
-        stages=["clip", "unet", "vae", "vae_encoder"],
-        image_height: int = 512,
-        image_width: int = 512,
-        max_batch_size: int = 16,
-        # ONNX export parameters
-        onnx_opset: int = 17,
-        onnx_dir: str = "onnx",
-        # TensorRT engine build parameters
-        engine_dir: str = "engine",
-        build_preview_features: bool = True,
-        force_engine_rebuild: bool = False,
-        timing_cache: str = "timing_cache",
-    ):
-        super().__init__(
-            vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker
-        )
-
-        self.vae.forward = self.vae.decode
-
-        self.stages = stages
-        self.image_height, self.image_width = image_height, image_width
-        self.inpaint = False
-        self.onnx_opset = onnx_opset
-        self.onnx_dir = onnx_dir
-        self.engine_dir = engine_dir
-        self.force_engine_rebuild = force_engine_rebuild
-        self.timing_cache = timing_cache
-        self.build_static_batch = False
-        self.build_dynamic_shape = False
-        self.build_preview_features = build_preview_features
-
-        self.max_batch_size = max_batch_size
-        # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation.
-        if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512:
-            self.max_batch_size = 4
-
-        self.stream = None  # loaded in loadResources()
-        self.models = {}  # loaded in __loadModels()
-        self.engine = {}  # loaded in build_engines()
-
-    def __loadModels(self):
-        # Load pipeline models
-        self.embedding_dim = self.text_encoder.config.hidden_size
-        models_args = {
-            "device": self.torch_device,
-            "max_batch_size": self.max_batch_size,
-            "embedding_dim": self.embedding_dim,
-            "inpaint": self.inpaint,
-        }
-        if "clip" in self.stages:
-            self.models["clip"] = make_CLIP(self.text_encoder, **models_args)
-        if "unet" in self.stages:
-            self.models["unet"] = make_UNet(self.unet, **models_args)
-        if "vae" in self.stages:
-            self.models["vae"] = make_VAE(self.vae, **models_args)
-        if "vae_encoder" in self.stages:
-            self.models["vae_encoder"] = make_VAEEncoder(self.vae, **models_args)
-
-    @classmethod
-    def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
-        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
-        resume_download = kwargs.pop("resume_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)
-
-        cls.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,
-            )
-        )
-
-    def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False):
-        super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings)
-
-        self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir)
-        self.engine_dir = os.path.join(self.cached_folder, self.engine_dir)
-        self.timing_cache = os.path.join(self.cached_folder, self.timing_cache)
-
-        # set device
-        self.torch_device = self._execution_device
-        logger.warning(f"Running inference on device: {self.torch_device}")
-
-        # load models
-        self.__loadModels()
-
-        # build engines
-        self.engine = build_engines(
-            self.models,
-            self.engine_dir,
-            self.onnx_dir,
-            self.onnx_opset,
-            opt_image_height=self.image_height,
-            opt_image_width=self.image_width,
-            force_engine_rebuild=self.force_engine_rebuild,
-            static_batch=self.build_static_batch,
-            static_shape=not self.build_dynamic_shape,
-            enable_preview=self.build_preview_features,
-            timing_cache=self.timing_cache,
-        )
-
-        return self
-
-    def __initialize_timesteps(self, timesteps, strength):
-        self.scheduler.set_timesteps(timesteps)
-        offset = self.scheduler.steps_offset if hasattr(self.scheduler, "steps_offset") else 0
-        init_timestep = int(timesteps * strength) + offset
-        init_timestep = min(init_timestep, timesteps)
-        t_start = max(timesteps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:].to(self.torch_device)
-        return timesteps, t_start
-
-    def __preprocess_images(self, batch_size, images=()):
-        init_images = []
-        for image in images:
-            image = image.to(self.torch_device).float()
-            image = image.repeat(batch_size, 1, 1, 1)
-            init_images.append(image)
-        return tuple(init_images)
-
-    def __encode_image(self, init_image):
-        init_latents = runEngine(self.engine["vae_encoder"], {"images": device_view(init_image)}, self.stream)[
-            "latent"
-        ]
-        init_latents = 0.18215 * init_latents
-        return init_latents
-
-    def __encode_prompt(self, prompt, negative_prompt):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-             prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-        """
-        # Tokenize prompt
-        text_input_ids = (
-            self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            .input_ids.type(torch.int32)
-            .to(self.torch_device)
-        )
-
-        text_input_ids_inp = device_view(text_input_ids)
-        # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt
-        text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[
-            "text_embeddings"
-        ].clone()
-
-        # Tokenize negative prompt
-        uncond_input_ids = (
-            self.tokenizer(
-                negative_prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            .input_ids.type(torch.int32)
-            .to(self.torch_device)
-        )
-        uncond_input_ids_inp = device_view(uncond_input_ids)
-        uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[
-            "text_embeddings"
-        ]
-
-        # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance
-        text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16)
-
-        return text_embeddings
-
-    def __denoise_latent(
-        self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None
-    ):
-        if not isinstance(timesteps, torch.Tensor):
-            timesteps = self.scheduler.timesteps
-        for step_index, timestep in enumerate(timesteps):
-            # Expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2)
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep)
-            if isinstance(mask, torch.Tensor):
-                latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
-
-            # Predict the noise residual
-            timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep
-
-            sample_inp = device_view(latent_model_input)
-            timestep_inp = device_view(timestep_float)
-            embeddings_inp = device_view(text_embeddings)
-            noise_pred = runEngine(
-                self.engine["unet"],
-                {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp},
-                self.stream,
-            )["latent"]
-
-            # Perform guidance
-            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-            noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-            latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample
-
-        latents = 1.0 / 0.18215 * latents
-        return latents
-
-    def __decode_latent(self, latents):
-        images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"]
-        images = (images / 2 + 0.5).clamp(0, 1)
-        return images.cpu().permute(0, 2, 3, 1).float().numpy()
-
-    def __loadResources(self, image_height, image_width, batch_size):
-        self.stream = cuda.Stream()
-
-        # Allocate buffers for TensorRT engine bindings
-        for model_name, obj in self.models.items():
-            self.engine[model_name].allocate_buffers(
-                shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device
-            )
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
-        strength: float = 0.8,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            image (`PIL.Image.Image`):
-                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
-                be masked out with `mask_image` and repainted according to `prompt`.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead.
-                Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-
-        """
-        self.generator = generator
-        self.denoising_steps = num_inference_steps
-        self.guidance_scale = guidance_scale
-
-        # Pre-compute latent input scales and linear multistep coefficients
-        self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device)
-
-        # Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-            prompt = [prompt]
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            raise ValueError(f"Expected prompt to be of type list or str but got {type(prompt)}")
-
-        if negative_prompt is None:
-            negative_prompt = [""] * batch_size
-
-        if negative_prompt is not None and isinstance(negative_prompt, str):
-            negative_prompt = [negative_prompt]
-
-        assert len(prompt) == len(negative_prompt)
-
-        if batch_size > self.max_batch_size:
-            raise ValueError(
-                f"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4"
-            )
-
-        # load resources
-        self.__loadResources(self.image_height, self.image_width, batch_size)
-
-        with torch.inference_mode(), torch.autocast("cuda"), trt.Runtime(TRT_LOGGER):
-            # Initialize timesteps
-            timesteps, t_start = self.__initialize_timesteps(self.denoising_steps, strength)
-            latent_timestep = timesteps[:1].repeat(batch_size)
-
-            # Pre-process input image
-            if isinstance(image, PIL.Image.Image):
-                image = preprocess_image(image)
-            init_image = self.__preprocess_images(batch_size, (image,))[0]
-
-            # VAE encode init image
-            init_latents = self.__encode_image(init_image)
-
-            # Add noise to latents using timesteps
-            noise = torch.randn(
-                init_latents.shape, generator=self.generator, device=self.torch_device, dtype=torch.float32
-            )
-            latents = self.scheduler.add_noise(init_latents, noise, latent_timestep)
-
-            # CLIP text encoder
-            text_embeddings = self.__encode_prompt(prompt, negative_prompt)
-
-            # UNet denoiser
-            latents = self.__denoise_latent(latents, text_embeddings, timesteps=timesteps, step_offset=t_start)
-
-            # VAE decode latent
-            images = self.__decode_latent(latents)
-
-        images = self.numpy_to_pil(images)
-        return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=None)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_models_diffuser_to_diffusers.py

@@ -1,100 +0,0 @@
-import json
-import os
-
-import torch
-
-from diffusers import UNet1DModel
-
-
-os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
-os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
-
-os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
-
-
-def unet(hor):
-    if hor == 128:
-        down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
-        block_out_channels = (32, 128, 256)
-        up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D")
-
-    elif hor == 32:
-        down_block_types = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
-        block_out_channels = (32, 64, 128, 256)
-        up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
-    model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch")
-    state_dict = model.state_dict()
-    config = {
-        "down_block_types": down_block_types,
-        "block_out_channels": block_out_channels,
-        "up_block_types": up_block_types,
-        "layers_per_block": 1,
-        "use_timestep_embedding": True,
-        "out_block_type": "OutConv1DBlock",
-        "norm_num_groups": 8,
-        "downsample_each_block": False,
-        "in_channels": 14,
-        "out_channels": 14,
-        "extra_in_channels": 0,
-        "time_embedding_type": "positional",
-        "flip_sin_to_cos": False,
-        "freq_shift": 1,
-        "sample_size": 65536,
-        "mid_block_type": "MidResTemporalBlock1D",
-        "act_fn": "mish",
-    }
-    hf_value_function = UNet1DModel(**config)
-    print(f"length of state dict: {len(state_dict.keys())}")
-    print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
-    mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys()))
-    for k, v in mapping.items():
-        state_dict[v] = state_dict.pop(k)
-    hf_value_function.load_state_dict(state_dict)
-
-    torch.save(hf_value_function.state_dict(), f"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin")
-    with open(f"hub/hopper-medium-v2/unet/hor{hor}/config.json", "w") as f:
-        json.dump(config, f)
-
-
-def value_function():
-    config = {
-        "in_channels": 14,
-        "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
-        "up_block_types": (),
-        "out_block_type": "ValueFunction",
-        "mid_block_type": "ValueFunctionMidBlock1D",
-        "block_out_channels": (32, 64, 128, 256),
-        "layers_per_block": 1,
-        "downsample_each_block": True,
-        "sample_size": 65536,
-        "out_channels": 14,
-        "extra_in_channels": 0,
-        "time_embedding_type": "positional",
-        "use_timestep_embedding": True,
-        "flip_sin_to_cos": False,
-        "freq_shift": 1,
-        "norm_num_groups": 8,
-        "act_fn": "mish",
-    }
-
-    model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch")
-    state_dict = model
-    hf_value_function = UNet1DModel(**config)
-    print(f"length of state dict: {len(state_dict.keys())}")
-    print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}")
-
-    mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys()))
-    for k, v in mapping.items():
-        state_dict[v] = state_dict.pop(k)
-
-    hf_value_function.load_state_dict(state_dict)
-
-    torch.save(hf_value_function.state_dict(), "hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin")
-    with open("hub/hopper-medium-v2/value_function/config.json", "w") as f:
-        json.dump(config, f)
-
-
-if __name__ == "__main__":
-    unet(32)
-    # unet(128)
-    value_function()

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/__init__.py

@@ -1,291 +0,0 @@
-__version__ = "0.19.3"
-
-from .configuration_utils import ConfigMixin
-from .utils import (
-    OptionalDependencyNotAvailable,
-    is_flax_available,
-    is_inflect_available,
-    is_invisible_watermark_available,
-    is_k_diffusion_available,
-    is_k_diffusion_version,
-    is_librosa_available,
-    is_note_seq_available,
-    is_onnx_available,
-    is_scipy_available,
-    is_torch_available,
-    is_torchsde_available,
-    is_transformers_available,
-    is_transformers_version,
-    is_unidecode_available,
-    logging,
-)
-
-
-try:
-    if not is_onnx_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_onnx_objects import *  # noqa F403
-else:
-    from .pipelines import OnnxRuntimeModel
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_pt_objects import *  # noqa F403
-else:
-    from .models import (
-        AsymmetricAutoencoderKL,
-        AutoencoderKL,
-        ControlNetModel,
-        ModelMixin,
-        MultiAdapter,
-        PriorTransformer,
-        T2IAdapter,
-        T5FilmDecoder,
-        Transformer2DModel,
-        UNet1DModel,
-        UNet2DConditionModel,
-        UNet2DModel,
-        UNet3DConditionModel,
-        VQModel,
-    )
-    from .optimization import (
-        get_constant_schedule,
-        get_constant_schedule_with_warmup,
-        get_cosine_schedule_with_warmup,
-        get_cosine_with_hard_restarts_schedule_with_warmup,
-        get_linear_schedule_with_warmup,
-        get_polynomial_decay_schedule_with_warmup,
-        get_scheduler,
-    )
-    from .pipelines import (
-        AudioPipelineOutput,
-        AutoPipelineForImage2Image,
-        AutoPipelineForInpainting,
-        AutoPipelineForText2Image,
-        ConsistencyModelPipeline,
-        DanceDiffusionPipeline,
-        DDIMPipeline,
-        DDPMPipeline,
-        DiffusionPipeline,
-        DiTPipeline,
-        ImagePipelineOutput,
-        KarrasVePipeline,
-        LDMPipeline,
-        LDMSuperResolutionPipeline,
-        PNDMPipeline,
-        RePaintPipeline,
-        ScoreSdeVePipeline,
-    )
-    from .schedulers import (
-        CMStochasticIterativeScheduler,
-        DDIMInverseScheduler,
-        DDIMParallelScheduler,
-        DDIMScheduler,
-        DDPMParallelScheduler,
-        DDPMScheduler,
-        DEISMultistepScheduler,
-        DPMSolverMultistepInverseScheduler,
-        DPMSolverMultistepScheduler,
-        DPMSolverSinglestepScheduler,
-        EulerAncestralDiscreteScheduler,
-        EulerDiscreteScheduler,
-        HeunDiscreteScheduler,
-        IPNDMScheduler,
-        KarrasVeScheduler,
-        KDPM2AncestralDiscreteScheduler,
-        KDPM2DiscreteScheduler,
-        PNDMScheduler,
-        RePaintScheduler,
-        SchedulerMixin,
-        ScoreSdeVeScheduler,
-        UnCLIPScheduler,
-        UniPCMultistepScheduler,
-        VQDiffusionScheduler,
-    )
-    from .training_utils import EMAModel
-
-try:
-    if not (is_torch_available() and is_scipy_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
-else:
-    from .schedulers import LMSDiscreteScheduler
-
-try:
-    if not (is_torch_available() and is_torchsde_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
-else:
-    from .schedulers import DPMSolverSDEScheduler
-
-try:
-    if not (is_torch_available() and is_transformers_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
-else:
-    from .pipelines import (
-        AltDiffusionImg2ImgPipeline,
-        AltDiffusionPipeline,
-        AudioLDMPipeline,
-        CycleDiffusionPipeline,
-        IFImg2ImgPipeline,
-        IFImg2ImgSuperResolutionPipeline,
-        IFInpaintingPipeline,
-        IFInpaintingSuperResolutionPipeline,
-        IFPipeline,
-        IFSuperResolutionPipeline,
-        ImageTextPipelineOutput,
-        KandinskyCombinedPipeline,
-        KandinskyImg2ImgCombinedPipeline,
-        KandinskyImg2ImgPipeline,
-        KandinskyInpaintCombinedPipeline,
-        KandinskyInpaintPipeline,
-        KandinskyPipeline,
-        KandinskyPriorPipeline,
-        KandinskyV22CombinedPipeline,
-        KandinskyV22ControlnetImg2ImgPipeline,
-        KandinskyV22ControlnetPipeline,
-        KandinskyV22Img2ImgCombinedPipeline,
-        KandinskyV22Img2ImgPipeline,
-        KandinskyV22InpaintCombinedPipeline,
-        KandinskyV22InpaintPipeline,
-        KandinskyV22Pipeline,
-        KandinskyV22PriorEmb2EmbPipeline,
-        KandinskyV22PriorPipeline,
-        LDMTextToImagePipeline,
-        PaintByExamplePipeline,
-        SemanticStableDiffusionPipeline,
-        ShapEImg2ImgPipeline,
-        ShapEPipeline,
-        StableDiffusionAdapterPipeline,
-        StableDiffusionAttendAndExcitePipeline,
-        StableDiffusionControlNetImg2ImgPipeline,
-        StableDiffusionControlNetInpaintPipeline,
-        StableDiffusionControlNetPipeline,
-        StableDiffusionDepth2ImgPipeline,
-        StableDiffusionDiffEditPipeline,
-        StableDiffusionImageVariationPipeline,
-        StableDiffusionImg2ImgPipeline,
-        StableDiffusionInpaintPipeline,
-        StableDiffusionInpaintPipelineLegacy,
-        StableDiffusionInstructPix2PixPipeline,
-        StableDiffusionLatentUpscalePipeline,
-        StableDiffusionLDM3DPipeline,
-        StableDiffusionModelEditingPipeline,
-        StableDiffusionPanoramaPipeline,
-        StableDiffusionParadigmsPipeline,
-        StableDiffusionPipeline,
-        StableDiffusionPipelineSafe,
-        StableDiffusionPix2PixZeroPipeline,
-        StableDiffusionSAGPipeline,
-        StableDiffusionUpscalePipeline,
-        StableDiffusionXLControlNetPipeline,
-        StableDiffusionXLImg2ImgPipeline,
-        StableDiffusionXLInpaintPipeline,
-        StableDiffusionXLInstructPix2PixPipeline,
-        StableDiffusionXLPipeline,
-        StableUnCLIPImg2ImgPipeline,
-        StableUnCLIPPipeline,
-        TextToVideoSDPipeline,
-        TextToVideoZeroPipeline,
-        UnCLIPImageVariationPipeline,
-        UnCLIPPipeline,
-        UniDiffuserModel,
-        UniDiffuserPipeline,
-        UniDiffuserTextDecoder,
-        VersatileDiffusionDualGuidedPipeline,
-        VersatileDiffusionImageVariationPipeline,
-        VersatileDiffusionPipeline,
-        VersatileDiffusionTextToImagePipeline,
-        VideoToVideoSDPipeline,
-        VQDiffusionPipeline,
-    )
-
-try:
-    if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
-else:
-    from .pipelines import StableDiffusionKDiffusionPipeline
-
-try:
-    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
-else:
-    from .pipelines import (
-        OnnxStableDiffusionImg2ImgPipeline,
-        OnnxStableDiffusionInpaintPipeline,
-        OnnxStableDiffusionInpaintPipelineLegacy,
-        OnnxStableDiffusionPipeline,
-        OnnxStableDiffusionUpscalePipeline,
-        StableDiffusionOnnxPipeline,
-    )
-
-try:
-    if not (is_torch_available() and is_librosa_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
-else:
-    from .pipelines import AudioDiffusionPipeline, Mel
-
-try:
-    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
-else:
-    from .pipelines import SpectrogramDiffusionPipeline
-
-try:
-    if not is_flax_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_flax_objects import *  # noqa F403
-else:
-    from .models.controlnet_flax import FlaxControlNetModel
-    from .models.modeling_flax_utils import FlaxModelMixin
-    from .models.unet_2d_condition_flax import FlaxUNet2DConditionModel
-    from .models.vae_flax import FlaxAutoencoderKL
-    from .pipelines import FlaxDiffusionPipeline
-    from .schedulers import (
-        FlaxDDIMScheduler,
-        FlaxDDPMScheduler,
-        FlaxDPMSolverMultistepScheduler,
-        FlaxKarrasVeScheduler,
-        FlaxLMSDiscreteScheduler,
-        FlaxPNDMScheduler,
-        FlaxSchedulerMixin,
-        FlaxScoreSdeVeScheduler,
-    )
-
-
-try:
-    if not (is_flax_available() and is_transformers_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
-else:
-    from .pipelines import (
-        FlaxStableDiffusionControlNetPipeline,
-        FlaxStableDiffusionImg2ImgPipeline,
-        FlaxStableDiffusionInpaintPipeline,
-        FlaxStableDiffusionPipeline,
-    )
-
-try:
-    if not (is_note_seq_available()):
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    from .utils.dummy_note_seq_objects import *  # noqa F403
-else:
-    from .pipelines import MidiProcessor

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py

@@ -1,409 +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.
-
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import PIL
-import torch
-from PIL import Image
-
-from ...models import UNet2DConditionModel, VQModel
-from ...schedulers import DDPMScheduler
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> import numpy as np
-
-        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
-        >>> from transformers import pipeline
-        >>> from diffusers.utils import load_image
-
-
-        >>> def make_hint(image, depth_estimator):
-        ...     image = depth_estimator(image)["depth"]
-        ...     image = np.array(image)
-        ...     image = image[:, :, None]
-        ...     image = np.concatenate([image, image, image], axis=2)
-        ...     detected_map = torch.from_numpy(image).float() / 255.0
-        ...     hint = detected_map.permute(2, 0, 1)
-        ...     return hint
-
-
-        >>> depth_estimator = pipeline("depth-estimation")
-
-        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior = pipe_prior.to("cuda")
-
-        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
-        ... )
-        >>> pipe = pipe.to("cuda")
-
-        >>> img = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... ).resize((768, 768))
-
-
-        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
-
-        >>> prompt = "A robot, 4k photo"
-        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
-
-        >>> generator = torch.Generator(device="cuda").manual_seed(43)
-
-        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
-        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
-
-        >>> images = pipe(
-        ...     image=img,
-        ...     strength=0.5,
-        ...     image_embeds=img_emb.image_embeds,
-        ...     negative_image_embeds=negative_emb.image_embeds,
-        ...     hint=hint,
-        ...     num_inference_steps=50,
-        ...     generator=generator,
-        ...     height=768,
-        ...     width=768,
-        ... ).images
-
-        >>> images[0].save("robot_cat.png")
-        ```
-"""
-
-
-# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
-def downscale_height_and_width(height, width, scale_factor=8):
-    new_height = height // scale_factor**2
-    if height % scale_factor**2 != 0:
-        new_height += 1
-    new_width = width // scale_factor**2
-    if width % scale_factor**2 != 0:
-        new_width += 1
-    return new_height * scale_factor, new_width * scale_factor
-
-
-# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
-def prepare_image(pil_image, w=512, h=512):
-    pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
-    arr = np.array(pil_image.convert("RGB"))
-    arr = arr.astype(np.float32) / 127.5 - 1
-    arr = np.transpose(arr, [2, 0, 1])
-    image = torch.from_numpy(arr).unsqueeze(0)
-    return image
-
-
-class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
-    """
-    Pipeline for image-to-image generation using Kandinsky
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        scheduler ([`DDIMScheduler`]):
-            A scheduler to be used in combination with `unet` to generate image latents.
-        unet ([`UNet2DConditionModel`]):
-            Conditional U-Net architecture to denoise the image embedding.
-        movq ([`VQModel`]):
-            MoVQ Decoder to generate the image from the latents.
-    """
-
-    def __init__(
-        self,
-        unet: UNet2DConditionModel,
-        scheduler: DDPMScheduler,
-        movq: VQModel,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            unet=unet,
-            scheduler=scheduler,
-            movq=movq,
-        )
-        self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
-
-    # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
-    def get_timesteps(self, num_inference_steps, strength, device):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps, num_inference_steps - t_start
-
-    # Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2_img2img.KandinskyV22Img2ImgPipeline.prepare_latents
-    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
-        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
-            raise ValueError(
-                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
-            )
-
-        image = image.to(device=device, dtype=dtype)
-
-        batch_size = batch_size * num_images_per_prompt
-
-        if image.shape[1] == 4:
-            init_latents = image
-
-        else:
-            if isinstance(generator, list) and len(generator) != batch_size:
-                raise ValueError(
-                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                )
-
-            elif isinstance(generator, list):
-                init_latents = [
-                    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
-                ]
-                init_latents = torch.cat(init_latents, dim=0)
-            else:
-                init_latents = self.movq.encode(image).latent_dist.sample(generator)
-
-            init_latents = self.movq.config.scaling_factor * init_latents
-
-        init_latents = torch.cat([init_latents], dim=0)
-
-        shape = init_latents.shape
-        noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-
-        # get latents
-        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-
-        latents = init_latents
-
-        return latents
-
-    # Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.KandinskyV22Pipeline.enable_model_cpu_offload
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.unet, self.movq]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        image: Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]],
-        negative_image_embeds: Union[torch.FloatTensor, List[torch.FloatTensor]],
-        hint: torch.FloatTensor,
-        height: int = 512,
-        width: int = 512,
-        num_inference_steps: int = 100,
-        guidance_scale: float = 4.0,
-        strength: float = 0.3,
-        num_images_per_prompt: int = 1,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        output_type: Optional[str] = "pil",
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for text prompt, that will be used to condition the image generation.
-            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
-                `Image`, or tensor representing an image batch, that will be used as the starting point for the
-                process. Can also accpet image latents as `image`, if passing latents directly, it will not be encoded
-                again.
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
-            hint (`torch.FloatTensor`):
-                The controlnet condition.
-            negative_image_embeds (`torch.FloatTensor` or `List[torch.FloatTensor]`):
-                The clip image embeddings for negative text prompt, will be used to condition the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
-                (`np.array`) or `"pt"` (`torch.Tensor`).
-            callback (`Callable`, *optional*):
-                A function that calls every `callback_steps` steps during inference. The function is called with the
-                following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function is called. If not specified, the callback is called at
-                every step.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.ImagePipelineOutput`] or `tuple`
-        """
-        device = self._execution_device
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        if isinstance(image_embeds, list):
-            image_embeds = torch.cat(image_embeds, dim=0)
-        if isinstance(negative_image_embeds, list):
-            negative_image_embeds = torch.cat(negative_image_embeds, dim=0)
-        if isinstance(hint, list):
-            hint = torch.cat(hint, dim=0)
-
-        batch_size = image_embeds.shape[0]
-
-        if do_classifier_free_guidance:
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            negative_image_embeds = negative_image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            hint = hint.repeat_interleave(num_images_per_prompt, dim=0)
-
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0).to(
-                dtype=self.unet.dtype, device=device
-            )
-            hint = torch.cat([hint, hint], dim=0).to(dtype=self.unet.dtype, device=device)
-
-        if not isinstance(image, list):
-            image = [image]
-        if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
-            raise ValueError(
-                f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support  PIL image and pytorch tensor"
-            )
-
-        image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
-        image = image.to(dtype=image_embeds.dtype, device=device)
-
-        latents = self.movq.encode(image)["latents"]
-        latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
-        latents = self.prepare_latents(
-            latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
-        )
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            added_cond_kwargs = {"image_embeds": image_embeds, "hint": hint}
-            noise_pred = self.unet(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=None,
-                added_cond_kwargs=added_cond_kwargs,
-                return_dict=False,
-            )[0]
-
-            if do_classifier_free_guidance:
-                noise_pred, variance_pred = noise_pred.split(latents.shape[1], dim=1)
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                _, variance_pred_text = variance_pred.chunk(2)
-                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, variance_pred_text], dim=1)
-
-            if not (
-                hasattr(self.scheduler.config, "variance_type")
-                and self.scheduler.config.variance_type in ["learned", "learned_range"]
-            ):
-                noise_pred, _ = noise_pred.split(latents.shape[1], dim=1)
-
-            # compute the previous noisy sample x_t -> x_t-1
-
-            latents = self.scheduler.step(
-                noise_pred,
-                t,
-                latents,
-                generator=generator,
-            )[0]
-
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        # post-processing
-        image = self.movq.decode(latents, force_not_quantize=True)["sample"]
-
-        # Offload last model to CPU
-        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-            self.final_offload_hook.offload()
-
-        if output_type not in ["pt", "np", "pil"]:
-            raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")
-
-        if output_type in ["np", "pil"]:
-            image = image * 0.5 + 0.5
-            image = image.clamp(0, 1)
-            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py

@@ -1,5 +0,0 @@
-_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))

spaces/Andy1621/uniformer_image_detection/mmdet/models/dense_heads/anchor_free_head.py

@@ -1,340 +0,0 @@
-from abc import abstractmethod
-
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import multi_apply
-from ..builder import HEADS, build_loss
-from .base_dense_head import BaseDenseHead
-from .dense_test_mixins import BBoxTestMixin
-
-
-@HEADS.register_module()
-class AnchorFreeHead(BaseDenseHead, BBoxTestMixin):
-    """Anchor-free head (FCOS, Fovea, RepPoints, etc.).
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        feat_channels (int): Number of hidden channels. Used in child classes.
-        stacked_convs (int): Number of stacking convs of the head.
-        strides (tuple): Downsample factor of each feature map.
-        dcn_on_last_conv (bool): If true, use dcn in the last layer of
-            towers. Default: False.
-        conv_bias (bool | str): If specified as `auto`, it will be decided by
-            the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
-            None, otherwise False. Default: "auto".
-        loss_cls (dict): Config of classification loss.
-        loss_bbox (dict): Config of localization loss.
-        conv_cfg (dict): Config dict for convolution layer. Default: None.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        train_cfg (dict): Training config of anchor head.
-        test_cfg (dict): Testing config of anchor head.
-    """  # noqa: W605
-
-    _version = 1
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 feat_channels=256,
-                 stacked_convs=4,
-                 strides=(4, 8, 16, 32, 64),
-                 dcn_on_last_conv=False,
-                 conv_bias='auto',
-                 loss_cls=dict(
-                     type='FocalLoss',
-                     use_sigmoid=True,
-                     gamma=2.0,
-                     alpha=0.25,
-                     loss_weight=1.0),
-                 loss_bbox=dict(type='IoULoss', loss_weight=1.0),
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 train_cfg=None,
-                 test_cfg=None):
-        super(AnchorFreeHead, self).__init__()
-        self.num_classes = num_classes
-        self.cls_out_channels = num_classes
-        self.in_channels = in_channels
-        self.feat_channels = feat_channels
-        self.stacked_convs = stacked_convs
-        self.strides = strides
-        self.dcn_on_last_conv = dcn_on_last_conv
-        assert conv_bias == 'auto' or isinstance(conv_bias, bool)
-        self.conv_bias = conv_bias
-        self.loss_cls = build_loss(loss_cls)
-        self.loss_bbox = build_loss(loss_bbox)
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.fp16_enabled = False
-
-        self._init_layers()
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self._init_cls_convs()
-        self._init_reg_convs()
-        self._init_predictor()
-
-    def _init_cls_convs(self):
-        """Initialize classification conv layers of the head."""
-        self.cls_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            if self.dcn_on_last_conv and i == self.stacked_convs - 1:
-                conv_cfg = dict(type='DCNv2')
-            else:
-                conv_cfg = self.conv_cfg
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    bias=self.conv_bias))
-
-    def _init_reg_convs(self):
-        """Initialize bbox regression conv layers of the head."""
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            if self.dcn_on_last_conv and i == self.stacked_convs - 1:
-                conv_cfg = dict(type='DCNv2')
-            else:
-                conv_cfg = self.conv_cfg
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    bias=self.conv_bias))
-
-    def _init_predictor(self):
-        """Initialize predictor layers of the head."""
-        self.conv_cls = nn.Conv2d(
-            self.feat_channels, self.cls_out_channels, 3, padding=1)
-        self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            if isinstance(m.conv, nn.Conv2d):
-                normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            if isinstance(m.conv, nn.Conv2d):
-                normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.conv_cls, std=0.01, bias=bias_cls)
-        normal_init(self.conv_reg, std=0.01)
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        """Hack some keys of the model state dict so that can load checkpoints
-        of previous version."""
-        version = local_metadata.get('version', None)
-        if version is None:
-            # the key is different in early versions
-            # for example, 'fcos_cls' become 'conv_cls' now
-            bbox_head_keys = [
-                k for k in state_dict.keys() if k.startswith(prefix)
-            ]
-            ori_predictor_keys = []
-            new_predictor_keys = []
-            # e.g. 'fcos_cls' or 'fcos_reg'
-            for key in bbox_head_keys:
-                ori_predictor_keys.append(key)
-                key = key.split('.')
-                conv_name = None
-                if key[1].endswith('cls'):
-                    conv_name = 'conv_cls'
-                elif key[1].endswith('reg'):
-                    conv_name = 'conv_reg'
-                elif key[1].endswith('centerness'):
-                    conv_name = 'conv_centerness'
-                else:
-                    assert NotImplementedError
-                if conv_name is not None:
-                    key[1] = conv_name
-                    new_predictor_keys.append('.'.join(key))
-                else:
-                    ori_predictor_keys.pop(-1)
-            for i in range(len(new_predictor_keys)):
-                state_dict[new_predictor_keys[i]] = state_dict.pop(
-                    ori_predictor_keys[i])
-        super()._load_from_state_dict(state_dict, prefix, local_metadata,
-                                      strict, missing_keys, unexpected_keys,
-                                      error_msgs)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually contain classification scores and bbox predictions.
-                cls_scores (list[Tensor]): Box scores for each scale level,
-                    each is a 4D-tensor, the channel number is
-                    num_points * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                    level, each is a 4D-tensor, the channel number is
-                    num_points * 4.
-        """
-        return multi_apply(self.forward_single, feats)[:2]
-
-    def forward_single(self, x):
-        """Forward features of a single scale level.
-
-        Args:
-            x (Tensor): FPN feature maps of the specified stride.
-
-        Returns:
-            tuple: Scores for each class, bbox predictions, features
-                after classification and regression conv layers, some
-                models needs these features like FCOS.
-        """
-        cls_feat = x
-        reg_feat = x
-
-        for cls_layer in self.cls_convs:
-            cls_feat = cls_layer(cls_feat)
-        cls_score = self.conv_cls(cls_feat)
-
-        for reg_layer in self.reg_convs:
-            reg_feat = reg_layer(reg_feat)
-        bbox_pred = self.conv_reg(reg_feat)
-        return cls_score, bbox_pred, cls_feat, reg_feat
-
-    @abstractmethod
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute loss of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level,
-                each is a 4D-tensor, the channel number is
-                num_points * num_classes.
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level, each is a 4D-tensor, the channel number is
-                num_points * 4.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-        """
-
-        raise NotImplementedError
-
-    @abstractmethod
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def get_bboxes(self,
-                   cls_scores,
-                   bbox_preds,
-                   img_metas,
-                   cfg=None,
-                   rescale=None):
-        """Transform network output for a batch into bbox predictions.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_points * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_points * 4, H, W)
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            cfg (mmcv.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used
-            rescale (bool): If True, return boxes in original image space
-        """
-
-        raise NotImplementedError
-
-    @abstractmethod
-    def get_targets(self, points, gt_bboxes_list, gt_labels_list):
-        """Compute regression, classification and centerness targets for points
-        in multiple images.
-
-        Args:
-            points (list[Tensor]): Points of each fpn level, each has shape
-                (num_points, 2).
-            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
-                each has shape (num_gt, 4).
-            gt_labels_list (list[Tensor]): Ground truth labels of each box,
-                each has shape (num_gt,).
-        """
-        raise NotImplementedError
-
-    def _get_points_single(self,
-                           featmap_size,
-                           stride,
-                           dtype,
-                           device,
-                           flatten=False):
-        """Get points of a single scale level."""
-        h, w = featmap_size
-        x_range = torch.arange(w, dtype=dtype, device=device)
-        y_range = torch.arange(h, dtype=dtype, device=device)
-        y, x = torch.meshgrid(y_range, x_range)
-        if flatten:
-            y = y.flatten()
-            x = x.flatten()
-        return y, x
-
-    def get_points(self, featmap_sizes, dtype, device, flatten=False):
-        """Get points according to feature map sizes.
-
-        Args:
-            featmap_sizes (list[tuple]): Multi-level feature map sizes.
-            dtype (torch.dtype): Type of points.
-            device (torch.device): Device of points.
-
-        Returns:
-            tuple: points of each image.
-        """
-        mlvl_points = []
-        for i in range(len(featmap_sizes)):
-            mlvl_points.append(
-                self._get_points_single(featmap_sizes[i], self.strides[i],
-                                        dtype, device, flatten))
-        return mlvl_points
-
-    def aug_test(self, feats, img_metas, rescale=False):
-        """Test function with test time augmentation.
-
-        Args:
-            feats (list[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains features for all images in the batch.
-            img_metas (list[list[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. each dict has image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[ndarray]: bbox results of each class
-        """
-        return self.aug_test_bboxes(feats, img_metas, rescale=rescale)

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_480x480_40k_pascal_context_59.py

@@ -1,10 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_r50-d8.py',
-    '../_base_/datasets/pascal_context_59.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=59),
-    auxiliary_head=dict(num_classes=59),
-    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
-optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

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

@@ -1,16 +0,0 @@
-from typing import List, TypeVar
-
-T = TypeVar("T")
-
-
-class Stack(List[T]):
-    """A small shim over builtin list."""
-
-    @property
-    def top(self) -> T:
-        """Get top of stack."""
-        return self[-1]
-
-    def push(self, item: T) -> None:
-        """Push an item on to the stack (append in stack nomenclature)."""
-        self.append(item)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/extension.py

@@ -1,148 +0,0 @@
-import re
-import functools
-import distutils.core
-import distutils.errors
-import distutils.extension
-
-from .monkey import get_unpatched
-
-
-def _have_cython():
-    """
-    Return True if Cython can be imported.
-    """
-    cython_impl = 'Cython.Distutils.build_ext'
-    try:
-        # from (cython_impl) import build_ext
-        __import__(cython_impl, fromlist=['build_ext']).build_ext
-        return True
-    except Exception:
-        pass
-    return False
-
-
-# for compatibility
-have_pyrex = _have_cython
-
-_Extension = get_unpatched(distutils.core.Extension)
-
-
-class Extension(_Extension):
-    """
-    Describes a single extension module.
-
-    This means that all source files will be compiled into a single binary file
-    ``<module path>.<suffix>`` (with ``<module path>`` derived from ``name`` and
-    ``<suffix>`` defined by one of the values in
-    ``importlib.machinery.EXTENSION_SUFFIXES``).
-
-    In the case ``.pyx`` files are passed as ``sources and`` ``Cython`` is **not**
-    installed in the build environment, ``setuptools`` may also try to look for the
-    equivalent ``.cpp`` or ``.c`` files.
-
-    :arg str name:
-      the full name of the extension, including any packages -- ie.
-      *not* a filename or pathname, but Python dotted name
-
-    :arg list[str] sources:
-      list of source filenames, relative to the distribution root
-      (where the setup script lives), in Unix form (slash-separated)
-      for portability.  Source files may be C, C++, SWIG (.i),
-      platform-specific resource files, or whatever else is recognized
-      by the "build_ext" command as source for a Python extension.
-
-    :keyword list[str] include_dirs:
-      list of directories to search for C/C++ header files (in Unix
-      form for portability)
-
-    :keyword list[tuple[str, str|None]] define_macros:
-      list of macros to define; each macro is defined using a 2-tuple:
-      the first item corresponding to the name of the macro and the second
-      item either a string with its value or None to
-      define it without a particular value (equivalent of "#define
-      FOO" in source or -DFOO on Unix C compiler command line)
-
-    :keyword list[str] undef_macros:
-      list of macros to undefine explicitly
-
-    :keyword list[str] library_dirs:
-      list of directories to search for C/C++ libraries at link time
-
-    :keyword list[str] libraries:
-      list of library names (not filenames or paths) to link against
-
-    :keyword list[str] runtime_library_dirs:
-      list of directories to search for C/C++ libraries at run time
-      (for shared extensions, this is when the extension is loaded).
-      Setting this will cause an exception during build on Windows
-      platforms.
-
-    :keyword list[str] extra_objects:
-      list of extra files to link with (eg. object files not implied
-      by 'sources', static library that must be explicitly specified,
-      binary resource files, etc.)
-
-    :keyword list[str] extra_compile_args:
-      any extra platform- and compiler-specific information to use
-      when compiling the source files in 'sources'.  For platforms and
-      compilers where "command line" makes sense, this is typically a
-      list of command-line arguments, but for other platforms it could
-      be anything.
-
-    :keyword list[str] extra_link_args:
-      any extra platform- and compiler-specific information to use
-      when linking object files together to create the extension (or
-      to create a new static Python interpreter).  Similar
-      interpretation as for 'extra_compile_args'.
-
-    :keyword list[str] export_symbols:
-      list of symbols to be exported from a shared extension.  Not
-      used on all platforms, and not generally necessary for Python
-      extensions, which typically export exactly one symbol: "init" +
-      extension_name.
-
-    :keyword list[str] swig_opts:
-      any extra options to pass to SWIG if a source file has the .i
-      extension.
-
-    :keyword list[str] depends:
-      list of files that the extension depends on
-
-    :keyword str language:
-      extension language (i.e. "c", "c++", "objc"). Will be detected
-      from the source extensions if not provided.
-
-    :keyword bool optional:
-      specifies that a build failure in the extension should not abort the
-      build process, but simply not install the failing extension.
-
-    :keyword bool py_limited_api:
-      opt-in flag for the usage of :doc:`Python's limited API <python:c-api/stable>`.
-
-    :raises setuptools.errors.PlatformError: if 'runtime_library_dirs' is
-      specified on Windows. (since v63)
-    """
-
-    def __init__(self, name, sources, *args, **kw):
-        # The *args is needed for compatibility as calls may use positional
-        # arguments. py_limited_api may be set only via keyword.
-        self.py_limited_api = kw.pop("py_limited_api", False)
-        super().__init__(name, sources, *args, **kw)
-
-    def _convert_pyx_sources_to_lang(self):
-        """
-        Replace sources with .pyx extensions to sources with the target
-        language extension. This mechanism allows language authors to supply
-        pre-converted sources but to prefer the .pyx sources.
-        """
-        if _have_cython():
-            # the build has Cython, so allow it to compile the .pyx files
-            return
-        lang = self.language or ''
-        target_ext = '.cpp' if lang.lower() == 'c++' else '.c'
-        sub = functools.partial(re.sub, '.pyx$', target_ext)
-        self.sources = list(map(sub, self.sources))
-
-
-class Library(Extension):
-    """Just like a regular Extension, but built as a library instead"""

spaces/AtomdffAI/wechatgpt4atom/README.md

@@ -1,13 +0,0 @@
----
-title: wechat-bot
-emoji: 👀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-duplicated_from: lewisliuX123/wechatgpt3
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/meta_arch/fcos.py

@@ -1,303 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-from typing import List, Optional, Tuple
-import torch
-from fvcore.nn import sigmoid_focal_loss_jit
-from torch import Tensor, nn
-from torch.nn import functional as F
-
-from detectron2.layers import ShapeSpec, batched_nms
-from detectron2.structures import Boxes, ImageList, Instances, pairwise_point_box_distance
-from detectron2.utils.events import get_event_storage
-
-from ..anchor_generator import DefaultAnchorGenerator
-from ..backbone import Backbone
-from ..box_regression import Box2BoxTransformLinear, _dense_box_regression_loss
-from .dense_detector import DenseDetector
-from .retinanet import RetinaNetHead
-
-__all__ = ["FCOS"]
-
-
-logger = logging.getLogger(__name__)
-
-
-class FCOS(DenseDetector):
-    """
-    Implement FCOS in :paper:`fcos`.
-    """
-
-    def __init__(
-        self,
-        *,
-        backbone: Backbone,
-        head: nn.Module,
-        head_in_features: Optional[List[str]] = None,
-        box2box_transform=None,
-        num_classes,
-        center_sampling_radius: float = 1.5,
-        focal_loss_alpha=0.25,
-        focal_loss_gamma=2.0,
-        test_score_thresh=0.2,
-        test_topk_candidates=1000,
-        test_nms_thresh=0.6,
-        max_detections_per_image=100,
-        pixel_mean,
-        pixel_std,
-    ):
-        """
-        Args:
-            center_sampling_radius: radius of the "center" of a groundtruth box,
-                within which all anchor points are labeled positive.
-            Other arguments mean the same as in :class:`RetinaNet`.
-        """
-        super().__init__(
-            backbone, head, head_in_features, pixel_mean=pixel_mean, pixel_std=pixel_std
-        )
-
-        self.num_classes = num_classes
-
-        # FCOS uses one anchor point per location.
-        # We represent the anchor point by a box whose size equals the anchor stride.
-        feature_shapes = backbone.output_shape()
-        fpn_strides = [feature_shapes[k].stride for k in self.head_in_features]
-        self.anchor_generator = DefaultAnchorGenerator(
-            sizes=[[k] for k in fpn_strides], aspect_ratios=[1.0], strides=fpn_strides
-        )
-
-        # FCOS parameterizes box regression by a linear transform,
-        # where predictions are normalized by anchor stride (equal to anchor size).
-        if box2box_transform is None:
-            box2box_transform = Box2BoxTransformLinear(normalize_by_size=True)
-        self.box2box_transform = box2box_transform
-
-        self.center_sampling_radius = float(center_sampling_radius)
-
-        # Loss parameters:
-        self.focal_loss_alpha = focal_loss_alpha
-        self.focal_loss_gamma = focal_loss_gamma
-
-        # Inference parameters:
-        self.test_score_thresh = test_score_thresh
-        self.test_topk_candidates = test_topk_candidates
-        self.test_nms_thresh = test_nms_thresh
-        self.max_detections_per_image = max_detections_per_image
-
-    def forward_training(self, images, features, predictions, gt_instances):
-        # Transpose the Hi*Wi*A dimension to the middle:
-        pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions(
-            predictions, [self.num_classes, 4, 1]
-        )
-        anchors = self.anchor_generator(features)
-        gt_labels, gt_boxes = self.label_anchors(anchors, gt_instances)
-        return self.losses(
-            anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness
-        )
-
-    @torch.no_grad()
-    def match_anchors(self, anchors: List[Boxes], gt_instances: List[Instances]):
-        """
-        Match anchors with ground truth boxes.
-
-        Args:
-            anchors: #level boxes, from the highest resolution to lower resolution
-            gt_instances: ground truth instances per image
-
-        Returns:
-            List[Tensor]:
-                #image tensors, each is a vector of matched gt
-                indices (or -1 for unmatched anchors) for all anchors.
-        """
-        num_anchors_per_level = [len(x) for x in anchors]
-        anchors = Boxes.cat(anchors)  # Rx4
-        anchor_centers = anchors.get_centers()  # Rx2
-        anchor_sizes = anchors.tensor[:, 2] - anchors.tensor[:, 0]  # R
-
-        lower_bound = anchor_sizes * 4
-        lower_bound[: num_anchors_per_level[0]] = 0
-        upper_bound = anchor_sizes * 8
-        upper_bound[-num_anchors_per_level[-1] :] = float("inf")
-
-        matched_indices = []
-        for gt_per_image in gt_instances:
-            gt_centers = gt_per_image.gt_boxes.get_centers()  # Nx2
-            # FCOS with center sampling: anchor point must be close enough to gt center.
-            pairwise_match = (anchor_centers[:, None, :] - gt_centers[None, :, :]).abs_().max(
-                dim=2
-            ).values < self.center_sampling_radius * anchor_sizes[:, None]
-            pairwise_dist = pairwise_point_box_distance(anchor_centers, gt_per_image.gt_boxes)
-
-            # The original FCOS anchor matching rule: anchor point must be inside gt
-            pairwise_match &= pairwise_dist.min(dim=2).values > 0
-
-            # Multilevel anchor matching in FCOS: each anchor is only responsible
-            # for certain scale range.
-            pairwise_dist = pairwise_dist.max(dim=2).values
-            pairwise_match &= (pairwise_dist > lower_bound[:, None]) & (
-                pairwise_dist < upper_bound[:, None]
-            )
-
-            # Match the GT box with minimum area, if there are multiple GT matches
-            gt_areas = gt_per_image.gt_boxes.area()  # N
-            pairwise_match = pairwise_match.to(torch.float32) * (1e8 - gt_areas[None, :])
-            min_values, matched_idx = pairwise_match.max(dim=1)  # R, per-anchor match
-            matched_idx[min_values < 1e-5] = -1  # Unmatched anchors are assigned -1
-
-            matched_indices.append(matched_idx)
-        return matched_indices
-
-    @torch.no_grad()
-    def label_anchors(self, anchors, gt_instances):
-        """
-        Same interface as :meth:`RetinaNet.label_anchors`, but implemented with FCOS
-        anchor matching rule.
-
-        Unlike RetinaNet, there are no ignored anchors.
-        """
-        matched_indices = self.match_anchors(anchors, gt_instances)
-
-        matched_labels, matched_boxes = [], []
-        for gt_index, gt_per_image in zip(matched_indices, gt_instances):
-            label = gt_per_image.gt_classes[gt_index.clip(min=0)]
-            label[gt_index < 0] = self.num_classes  # background
-
-            matched_gt_boxes = gt_per_image.gt_boxes[gt_index.clip(min=0)]
-
-            matched_labels.append(label)
-            matched_boxes.append(matched_gt_boxes)
-        return matched_labels, matched_boxes
-
-    def losses(
-        self, anchors, pred_logits, gt_labels, pred_anchor_deltas, gt_boxes, pred_centerness
-    ):
-        """
-        This method is almost identical to :meth:`RetinaNet.losses`, with an extra
-        "loss_centerness" in the returned dict.
-        """
-        num_images = len(gt_labels)
-        gt_labels = torch.stack(gt_labels)  # (N, R)
-
-        pos_mask = (gt_labels >= 0) & (gt_labels != self.num_classes)
-        num_pos_anchors = pos_mask.sum().item()
-        get_event_storage().put_scalar("num_pos_anchors", num_pos_anchors / num_images)
-        normalizer = self._ema_update("loss_normalizer", max(num_pos_anchors, 1), 300)
-
-        # classification and regression loss
-        gt_labels_target = F.one_hot(gt_labels, num_classes=self.num_classes + 1)[
-            :, :, :-1
-        ]  # no loss for the last (background) class
-        loss_cls = sigmoid_focal_loss_jit(
-            torch.cat(pred_logits, dim=1),
-            gt_labels_target.to(pred_logits[0].dtype),
-            alpha=self.focal_loss_alpha,
-            gamma=self.focal_loss_gamma,
-            reduction="sum",
-        )
-
-        loss_box_reg = _dense_box_regression_loss(
-            anchors,
-            self.box2box_transform,
-            pred_anchor_deltas,
-            [x.tensor for x in gt_boxes],
-            pos_mask,
-            box_reg_loss_type="giou",
-        )
-
-        ctrness_targets = self.compute_ctrness_targets(anchors, gt_boxes)  # NxR
-        pred_centerness = torch.cat(pred_centerness, dim=1).squeeze(dim=2)  # NxR
-        ctrness_loss = F.binary_cross_entropy_with_logits(
-            pred_centerness[pos_mask], ctrness_targets[pos_mask], reduction="sum"
-        )
-        return {
-            "loss_fcos_cls": loss_cls / normalizer,
-            "loss_fcos_loc": loss_box_reg / normalizer,
-            "loss_fcos_ctr": ctrness_loss / normalizer,
-        }
-
-    def compute_ctrness_targets(self, anchors, gt_boxes):  # NxR
-        anchors = Boxes.cat(anchors).tensor  # Rx4
-        reg_targets = [self.box2box_transform.get_deltas(anchors, m.tensor) for m in gt_boxes]
-        reg_targets = torch.stack(reg_targets, dim=0)  # NxRx4
-        if len(reg_targets) == 0:
-            return reg_targets.new_zeros(len(reg_targets))
-        left_right = reg_targets[:, :, [0, 2]]
-        top_bottom = reg_targets[:, :, [1, 3]]
-        ctrness = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (
-            top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]
-        )
-        return torch.sqrt(ctrness)
-
-    def forward_inference(
-        self, images: ImageList, features: List[Tensor], predictions: List[List[Tensor]]
-    ):
-        pred_logits, pred_anchor_deltas, pred_centerness = self._transpose_dense_predictions(
-            predictions, [self.num_classes, 4, 1]
-        )
-        anchors = self.anchor_generator(features)
-
-        results: List[Instances] = []
-        for img_idx, image_size in enumerate(images.image_sizes):
-            scores_per_image = [
-                # Multiply and sqrt centerness & classification scores
-                # (See eqn. 4 in https://arxiv.org/abs/2006.09214)
-                torch.sqrt(x[img_idx].sigmoid_() * y[img_idx].sigmoid_())
-                for x, y in zip(pred_logits, pred_centerness)
-            ]
-            deltas_per_image = [x[img_idx] for x in pred_anchor_deltas]
-            results_per_image = self.inference_single_image(
-                anchors, scores_per_image, deltas_per_image, image_size
-            )
-            results.append(results_per_image)
-        return results
-
-    def inference_single_image(
-        self,
-        anchors: List[Boxes],
-        box_cls: List[Tensor],
-        box_delta: List[Tensor],
-        image_size: Tuple[int, int],
-    ):
-        """
-        Identical to :meth:`RetinaNet.inference_single_image.
-        """
-        pred = self._decode_multi_level_predictions(
-            anchors,
-            box_cls,
-            box_delta,
-            self.test_score_thresh,
-            self.test_topk_candidates,
-            image_size,
-        )
-        keep = batched_nms(
-            pred.pred_boxes.tensor, pred.scores, pred.pred_classes, self.test_nms_thresh
-        )
-        return pred[keep[: self.max_detections_per_image]]
-
-
-class FCOSHead(RetinaNetHead):
-    """
-    The head used in :paper:`fcos`. It adds an additional centerness
-    prediction branch on top of :class:`RetinaNetHead`.
-    """
-
-    def __init__(self, *, input_shape: List[ShapeSpec], conv_dims: List[int], **kwargs):
-        super().__init__(input_shape=input_shape, conv_dims=conv_dims, num_anchors=1, **kwargs)
-        # Unlike original FCOS, we do not add an additional learnable scale layer
-        # because it's found to have no benefits after normalizing regression targets by stride.
-        self._num_features = len(input_shape)
-        self.ctrness = nn.Conv2d(conv_dims[-1], 1, kernel_size=3, stride=1, padding=1)
-        torch.nn.init.normal_(self.ctrness.weight, std=0.01)
-        torch.nn.init.constant_(self.ctrness.bias, 0)
-
-    def forward(self, features):
-        assert len(features) == self._num_features
-        logits = []
-        bbox_reg = []
-        ctrness = []
-        for feature in features:
-            logits.append(self.cls_score(self.cls_subnet(feature)))
-            bbox_feature = self.bbox_subnet(feature)
-            bbox_reg.append(self.bbox_pred(bbox_feature))
-            ctrness.append(self.ctrness(bbox_feature))
-        return logits, bbox_reg, ctrness

spaces/Benson/text-generation/Examples/Blockman Ir Aventura Hack Apk 2022 Cubos Ilimitados.md

@@ -1,83 +0,0 @@
-<br />
-<h1>Blockman Go aventura Hack APK 2022 cubos ilimitados</h1>
-<p>¿Te encanta jugar juegos basados en bloques con tus amigos? ¿Quieres explorar diferentes mundos y completar varios desafíos? Si es así, entonces deberías probar Blockman Go Adventure, un juego divertido y adictivo que te permite crear tu propio avatar, personalizar tus ajustes y unirte a millones de jugadores en línea. ¡Pero espera, hay más! También puede utilizar Blockman Go Aventura Hack APK, una versión modificada del juego que le da cubos ilimitados, monedas, gemas, y otros recursos. En este artículo, le diremos todo lo que necesita saber sobre Blockman Go Adventure y Blockman Go Adventure Hack APK, incluyendo sus características, cómo jugarlos, y algunos consejos y trucos para aprovechar al máximo su experiencia de juego. ¡Vamos a empezar! </p>
-<h2>blockman ir aventura hack apk 2022 cubos ilimitados</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6KrL">https://bltlly.com/2v6KrL</a></b></p><br /><br />
- <h2>¿Qué es Blockman Go Adventure? </h2>
-<p>Blockman Go Adventure es un juego online gratuito que combina elementos de sandbox, aventura y juegos sociales. Está desarrollado por Blockman GO Studio, un equipo de desarrolladores de juegos creativos y apasionados que tienen como objetivo proporcionar juegos de alta calidad para jugadores de todas las edades. Blockman Go Adventure es uno de sus juegos más populares, con más de 10 millones de descargas en Google Play Store y una calificación de 4.4 estrellas. </p>
- <h3>Características de Blockman Go Adventure</h3>
-<p>Blockman Go Adventure tiene muchas características que lo hacen un juego agradable y atractivo para todos. Algunas de estas características son:</p>
-<ul>
-<li><b>Múltiples minijuegos:</b> Puedes elegir entre más de 100 minijuegos que se adapten a tus preferencias y habilidades. Si te gustan los juegos de carreras, disparos, parkour o rompecabezas, encontrarás algo que te interesa en Blockman Go Adventure.</li>
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-
-<li><b>Interacción social:</b> Puedes chatear con otros jugadores en tiempo real usando mensajes de voz o texto. También puedes hacer amigos, enviar regalos y unirte a clanes. </li>
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-</ul>
- <h3>Cómo jugar Blockman Go Aventura</h3>
-<p>Jugar Blockman Go Adventure es fácil y divertido. Estos son los pasos a seguir:</p>
-<ol>
-<li>Descargar e instalar el juego desde Google Play Store o App Store.</li>
-<li>Crea una cuenta o inicia sesión con la existente. </li>
-<li>Selecciona un mini-juego desde el lobby o crea tu propia habitación. </li>
-<li>Invita a tus amigos o únete a otros jugadores en línea. </li>
-<li>Disfruta del juego y chatea con otros jugadores. </li>
-</ol>
- <h2>¿Qué es Blockman Go Aventura Hack APK? </h2>
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-<p></p>
-<ul>
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-</ul>
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-<ol>
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-</ol>
- <h2>Consejos y trucos para Blockman Go Aventura</h2>
-<p>Para hacer tu experiencia de juego más divertida y gratificante, aquí hay algunos consejos y trucos que puedes usar en Blockman Go Adventure:</p>
- <h3>Usa el menú mod para personalizar tu juego</h3>
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-
- <h3>Únete a un clan y juega con amigos</h3 <p>Otra forma de disfrutar de Blockman Go Adventure es unirse a un clan y jugar con amigos. Un clan es un grupo de jugadores que comparten un interés o objetivo común en el juego. Puedes unirte a un clan existente o crear uno propio. Al unirte a un clan, puedes chatear con otros miembros, enviar regalos, participar en guerras de clanes y ganar puntos de clan. También puedes invitar a tus amigos a unirse a tu clan o jugar con ellos en habitaciones privadas. Jugar con amigos puede hacer que el juego sea más divertido y social. </p>
- <h2>Conclusión</h2>
-<p>Blockman Go Adventure es un gran juego para cualquiera que ame los juegos basados en bloques con mucha variedad y creatividad. Puedes jugar diferentes minijuegos, explorar diferentes mundos, personalizar tu avatar e interactuar con otros jugadores en línea. También puede utilizar Blockman Go Aventura Hack APK para obtener recursos ilimitados y características que pueden mejorar su experiencia de juego. Sin embargo, debe tener cuidado al descargar e instalar estos archivos, ya que pueden contener virus o malware que pueden dañar su dispositivo o robar su información personal. También debe utilizar el truco de forma responsable y no abusar de él en las salas públicas o contra otros jugadores. </p>
- <h3>Resumen de los puntos principales</h3 <p>En este artículo, hemos cubierto los siguientes puntos:</p>
-<ul>
-<li>Blockman Go Adventure es un juego en línea gratuito que combina elementos de sandbox, aventura y juegos sociales. </li> <li>Blockman Go Aventura Hack APK es una versión modificada del juego que le da cubos ilimitados, monedas, gemas y otros recursos. </li>
-<li> Puede descargar e instalar Blockman Go Aventura Hack APK de sitios web de terceros, pero usted debe tener cuidado con los virus y el malware. </li>
-<li>Puedes usar el menú mod para personalizar la configuración de tu juego, como velocidad, gravedad, invisibilidad y más. </li>
-<li>Puedes recoger monedas y gemas para desbloquear nuevos objetos para tu avatar o actualizar los existentes. </li>
-
-</ul>
- <h3>Llamada a la acción</h3>
-<p>Si usted está interesado en jugar Blockman Go Aventura o Blockman Go Aventura Hack APK, puede descargarlos de los enlaces a continuación. También puede visitar el sitio web oficial o las páginas de redes sociales de Blockman GO Studio para obtener más información sobre sus juegos y actualizaciones. ¡Diviértete y disfruta de la aventura! </p>
-<ul>
-<li><a href="">Descargar Blockman Go Adventure de Google Play Store</a></li>
-<li><a href="">Descargar Blockman Go Adventure de App Store</a></li>
-<li><a href="">Descargar Blockman Go aventura Hack APK de HackDL</a></li>
-<li><a href="">Descargar Blockman Go aventura Hack APK de APKPure</a></li>
-<li><a href="">Visite el sitio web oficial de Blockman GO Studio</a></li>
-<li><a href=">Sigue a Blockman GO Studio en Facebook</a></li>
-<li><a href=">Sigue a Blockman GO Studio en Twitter</a></li>
-<li><a href=">Sigue a Blockman GO Studio en Instagram</a></li>
-</ul>
- <h4>Preguntas frecuentes</h4>
-<p>Aquí hay algunas preguntas frecuentes sobre Blockman Go Aventura y Blockman Go Aventura Hack APK:</p>
-<ol>
-¿Es seguro jugar a Blockman Go Adventure? </b><br>Sí, Blockman Go Adventure es seguro para jugar, ya que es desarrollado por un estudio de juegos de buena reputación y verificado por Google Play Store y App Store. Sin embargo, usted debe tener cuidado al descargar e instalar Blockman Go Aventura Hack APK, ya que puede contener virus o malware que pueden dañar su dispositivo o robar su información personal. </li>
-<li><b>¿Es Blockman Go Adventure gratis para jugar? </b><br>Sí, Blockman Go Adventure es gratis, pero contiene compras en la aplicación que le permiten comprar cubos, monedas, gemas y otros recursos. También puede utilizar Blockman Go Aventura Hack APK para obtener recursos ilimitados de forma gratuita. </li>
-
-<li><b> ¿Puedo jugar Blockman Go aventura en PC? </b><br>S��, puedes jugar Blockman Go Adventure en PC usando un emulador de Android, como BlueStacks o NoxPlayer. También puede utilizar el emulador para instalar y ejecutar Blockman Go Aventura Hack APK en su PC.</li>
-<li><b>¿Cómo puedo contactar a los desarrolladores de Blockman Go Adventure? </b><br>Puede ponerse en contacto con los desarrolladores de Blockman Go Adventure enviando un correo electrónico a [[email protected]] o rellenando el formulario de comentarios en su sitio web oficial. También puede seguirlos en sus páginas de redes sociales y dejar un comentario o mensaje allí. </li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cmo Descargar El ltimo Simulador De Conduccin De Coches En PC.md

@@ -1,128 +0,0 @@
-<br />
-<h1>Cómo descargar Ultimate Car Driving Simulator en PC</h1>
-<p>¿Te encantan los juegos de conducción? ¿Quieres experimentar la emoción de conducir coches realistas en un enorme mundo abierto? Si es así, entonces usted debe probar <strong>Ultimate Car Driving Simulator</strong>, uno de los mejores juegos de simulador de conducción de coches de 2020. En este artículo, le mostraremos cómo descargar Ultimate Car Driving Simulator en PC y disfrutarlo en una pantalla más grande con mejores gráficos y controles. </p>
-<h2>¿Qué es Ultimate Car Driving Simulator? </h2>
-<p>Ultimate Car Driving Simulator es un juego para móviles desarrollado por Sir Studios que combina el realismo y la diversión de la física de la conducción con una personalización ilimitada y un juego adictivo. Puedes elegir entre una gran variedad de coches, desde coches de carreras hasta vehículos todoterreno, todoterrenos, muscle cars, camiones, etc. y personalizarlos con innumerables vinilos, piezas, colores, etc. También puedes conducir en un enorme mapa de mundo abierto que tiene diferentes entornos, como ciudades, desiertos, montañas, bosques, etc. También puede disfrutar de diferentes modos de juego, como carreras, offroad, tráfico, punto de control, etc. y realizar acrobacias increíbles, derivas, saltos, etc. con física de conducción realista. Ultimate Car Driving Simulator es un juego gratuito, pero contiene anuncios y compras en la aplicación que puedes desactivar o comprar con dinero real. </p>
-<h2>cómo descargar el último simulador de conducción de coches en PC</h2><br /><p><b><b>Download Zip</b> &#9733;&#9733;&#9733; <a href="https://bltlly.com/2v6LBt">https://bltlly.com/2v6LBt</a></b></p><br /><br />
-<h2> ¿Por qué jugar Ultimate Car Driving Simulator en PC? </h2>
-<p>Si bien Ultimate Car Driving Simulator es un gran juego para jugar en su dispositivo móvil, es posible que se pregunte por qué debe jugar en su PC. Bueno, hay muchas razones para hacerlo, como:</p>
-<ul>
-<li><strong>Mejores gráficos y calidad de sonido</strong>: Jugando Ultimate Car Driving Simulator en PC le permitirá disfrutar de las impresionantes imágenes y efectos de sonido realistas del juego en alta resolución y pantalla completa. Usted será capaz de apreciar los detalles de los coches, los entornos, los efectos meteorológicos, etc. más claramente y sumergirse en el mundo del juego. </li>
-
-<li><strong>Pantalla más grande y más divertido</strong>: Jugar Ultimate Car Driving Simulator en PC también hará que su experiencia de juego sea más divertida y agradable. Puedes jugar el juego en una pantalla más grande y compartirlo con tus amigos y familiares. También puede grabar su juego, tomar capturas de pantalla, transmitir en línea, chatear con otros jugadores, etc. con facilidad. </li>
-</ul>
-<p>Como puedes ver, jugar Ultimate Car Driving Simulator en PC tiene muchas ventajas que mejorarán tu experiencia de juego. Entonces, ¿cómo se puede descargar y jugar Ultimate Car Driving Simulator en PC? Hay dos métodos principales que explicaremos en las siguientes secciones. </p>
-<h2>Cómo jugar último coche conducción simulador en PC con Windows 11</h2>
-<p>Si tienes un PC con Windows 11, estás de suerte porque puedes usar la función nativa de emulación de Android que viene con el nuevo sistema operativo. Esta función le permite ejecutar aplicaciones y juegos de Android en su PC sin ningún software o hardware adicional. Estos son los pasos para jugar Ultimate Car Driving Simulator en PC con Windows 11:</p>
-<ol>
-<li><strong>Abra la aplicación de Microsoft Store</strong> en su PC con Windows 11 y busque <strong>Simulador de conducción de automóviles definitivo</strong>. Alternativamente, puedes usar este enlace para ir directamente a la página del juego. </li>
-<li><strong>Haga clic en el botón Instalar</strong> para descargar e instalar el juego en su PC. Es posible que necesite iniciar sesión con su cuenta de Microsoft si aún no lo ha hecho. </li>
-<li><strong>Inicie el juego</strong> desde el menú Inicio o el acceso directo del escritorio. Verá una ventana emergente que le pide que habilite las aplicaciones de Android en su PC. Haga clic en <strong>Activar</strong>. </li>
-<li><strong>Inicia sesión con tu cuenta de Google</strong> para acceder a los Servicios de Google Play y sincronizar tus datos de juego y logros. Puede usar una cuenta existente o crear una nueva. </li>
-
-</ol>
-<p>¡Eso es todo! Has descargado y jugado con éxito Ultimate Car Driving Simulator en PC con la función de emulación nativa de Windows 11 para Android. Sin embargo, si no tiene un PC con Windows 11 o prefiere otro método, puede usar un emulador de Android para PC en su lugar. </p>
-<h2>Cómo jugar Ultimate Car Driving Simulator en PC con emuladores de Android</h2>
-<p>Un emulador de Android es un programa de software que simula un dispositivo Android en su PC. Le permite ejecutar aplicaciones y juegos de Android en su PC con características y funciones similares como un dispositivo Android real. Hay muchos emuladores de Android para PC disponibles en línea, pero no todos ellos son compatibles o optimizados para juegos. Por lo tanto, hemos seleccionado algunos de los mejores emuladores de Android para PC que puede utilizar para jugar Ultimate Car Driving Simulator en PC. Son:</p>
- <tabla>
-<tr>
-<th>Nombre</th>
-<th>Descripción</th>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td><strong>Bluestacks</strong></td>
-<td>Un emulador de Android popular y potente para PC que ha sido diseñado para juegos. Tiene una interfaz fácil de usar y muchas características y opciones para mejorar su experiencia de juego </td>
-<td>
-<ul>
-<li>Soporta juegos de gama alta con gráficos y rendimiento altos</li>
-<li>Ofrece una variedad de modos de juego, como Eco Mode, Multi-Instance, Macro Recorder, etc.</li>
-<li>Tiene una tienda de aplicaciones incorporada y un centro de juegos con miles de juegos</li>
-<li>Permite personalizar los controles, ajustes y preferencias del emulador y el juego</li>
-<li> Tiene una gran y activa comunidad de usuarios y desarrolladores</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Requiere un PC de gama alta con al menos 4GB de RAM y una GPU dedicada</li>
-<li>Consume muchos recursos de CPU y memoria</li>
-<li>Puede tener problemas de compatibilidad con algunos juegos o aplicaciones</li>
-<li>Puede tener anuncios o ventanas emergentes que pueden ser molestos o intrusivos</li>
-<li>Puede tener riesgos de seguridad o privacidad si no se descarga desde el sitio web oficial</li>
-</ul>
-</td>
-</tr>
-<tr>
-
-<td>Un emulador de Android rápido y suave para PC que también está diseñado para juegos. Tiene una interfaz simple e intuitiva y muchas características y opciones para mejorar tu experiencia de juego</td>
-<td>
-<ul>
-<li>Soporta la mayoría de los juegos con altos gráficos y rendimiento</li>
-<li>Ofrece una variedad de modos de juego, como Control de teclado, Registro de guiones, Multi-Drive, etc.</li>
-<li>Tiene una tienda de aplicaciones incorporada y un centro de juegos con miles de juegos</li>
-<li>Permite personalizar los controles, ajustes y preferencias del emulador y el juego</li>
-<li> Tiene una gran y activa comunidad de usuarios y desarrolladores</li>
-</ul>
-</td>
-<td>
-<ul>
-<li>Requiere un PC de gama alta con al menos 2GB de RAM y una GPU dedicada</li>
-<li>Consume muchos recursos de CPU y memoria</li>
-<li>Puede tener problemas de compatibilidad con algunos juegos o aplicaciones</li>
-<li>Puede tener anuncios o ventanas emergentes que pueden ser molestos o intrusivos</li>
-<li>Puede tener riesgos de seguridad o privacidad si no se descarga desde el sitio web oficial</li>
-</ul>
-</td>
-</tr>
-<tr>
-<td><strong>Gameloop</strong></td>
-<td>Un emulador de Android potente y optimizado para PC que está especialmente diseñado para juegos. Tiene una interfaz moderna y elegante y un montón de características y opciones para mejorar su experiencia de juego</td>
-<td>
-<ul>
-<li>Soporta la mayoría de los juegos con gráficos y rendimiento altos, especialmente juegos FPS y MOBA</li>
-<li>Ofrece una variedad de modos de juego, como Modo Turbo, Modo Inteligente, Modo Esports, etc.</li>
-<li>Tiene una tienda de aplicaciones incorporada y un centro de juegos con miles de juegos</li>
-<li>Permite personalizar los controles, ajustes y preferencias del emulador y el juego</li>
-<li> Tiene una gran y activa comunidad de usuarios y desarrolladores</li> </td>
-<td>
-<ul>
-<li>Requiere un PC de gama alta con al menos 4GB de RAM y una GPU dedicada</li>
-<li>Consume muchos recursos de CPU y memoria</li>
-<li>Puede tener problemas de compatibilidad con algunos juegos o aplicaciones</li>
-<li>Puede tener anuncios o ventanas emergentes que pueden ser molestos o intrusivos</li>
-
-</ul>
-</td>
-</tr>
-</tabla>
- <p>Como puedes ver, cada emulador de Android para PC tiene sus propios pros y contras, y puedes elegir el que se adapte a tus necesidades y preferencias. Estos son los pasos para jugar Ultimate Car Driving Simulator en PC con cualquiera de estos emuladores de Android:</p>
-<ol>
-<li><strong>Descargar e instalar el emulador de Android de su elección</strong> desde su sitio web oficial. Asegúrate de tener suficiente espacio y recursos en tu PC para ejecutar el emulador sin problemas. </li>
-<li><strong>Inicie el emulador</strong> e inicie sesión con su cuenta de Google para acceder a la Google Play Store y sincronizar los datos y logros del juego. Puede usar una cuenta existente o crear una nueva. </li>
-<li><strong>Búsqueda de Ultimate Car Driving Simulator</strong> en la tienda de Google Play o la tienda de aplicaciones del emulador e instalarlo en su PC.</li>
-<li><strong>Iniciar el juego</strong> desde la pantalla de inicio del emulador o el acceso directo del escritorio. Puede utilizar el teclado y el ratón o un controlador para conducir su coche. También puedes ajustar la configuración del juego y el emulador según tu preferencia. </li>
-<li><strong>Disfruta jugando Ultimate Car Driving Simulator en PC</strong> con cualquiera de estos emuladores de Android para PC. También puede grabar su juego, tomar capturas de pantalla, transmitir en línea, chatear con otros jugadores, etc. con facilidad. </li>
-</ol>
-<h2>Conclusión</h2>
-<p>En este artículo, le hemos mostrado cómo descargar Ultimate Car Driving Simulator en PC y disfrutarlo en una pantalla más grande con mejores gráficos y controles. Hemos explicado dos métodos principales para jugar Ultimate Car Driving Simulator en PC: usando la función nativa de emulación de Android de Windows 11 o usando un emulador de Android para PC. Ambos métodos son fáciles y eficaces, y usted puede elegir el que funciona mejor para usted. Esperamos que haya encontrado este artículo útil e informativo, y le animamos a probar Ultimate Car Driving Simulator en PC hoy. ¡No te arrepentirás! </p>
-<h2>Preguntas frecuentes</h2>
-
-<p>A1: Sí, es gratis para descargar y jugar, pero contiene anuncios y compras en la aplicación que puede desactivar o comprar con dinero real. </p>
-<p></p>
-<h3>Q2: ¿Cuáles son los requisitos mínimos para ejecutar Ultimate Car Driving Simulator en PC? </h3>
-<p>A2: depende del método que utilice, pero generalmente necesita un PC con Windows 10 o 11 con al menos 4 GB de RAM, un procesador Intel o AMD, una unidad de estado sólido con 10 GB de espacio libre y una GPU Intel UHD Graphics 630 o similar. </p>
-<h3>Q3: ¿Puedo jugar Ultimate Car Driving Simulator con un controlador o un teclado y ratón? </h3>
-<p>A3: Sí, puede usar cualquier dispositivo de entrada que sea compatible con su PC y el emulador que elija. También puede personalizar los controles según su preferencia. </p>
-<h3>Q4: ¿Puedo sincronizar mi progreso y mi biblioteca de juegos entre dispositivos? </h3>
-<p>A4: Sí, puede iniciar sesión con su cuenta de Google tanto en su dispositivo móvil como en su PC y acceder a sus datos y logros guardados. También puede cambiar entre dispositivos en cualquier momento sin perder su progreso. </p>
-<h3>Q5: ¿Cuáles son algunos consejos y trucos para mejorar mi juego en Ultimate Car Driving Simulator? </h3>
-<p>A5: Algunos consejos y trucos son: <ul>
-<li>Explora el mapa del mundo abierto y descubre diferentes terrenos, ciudades, desiertos, etc.</li>
-<li>Personaliza tu coche con varias partes, vinilos, colores, etc. para que sea único y elegante. </li>
-<li>Utilice la física de conducción realista para realizar acrobacias, derivas, saltos, etc. y ganar monedas y recompensas. </li>
-<li>Actualizar el motor de su coche, suspensión, frenos, neumáticos, etc. para mejorar su rendimiento y velocidad. </li>
-<li>Ponte a prueba con diferentes modos de juego, como carreras, offroad, tráfico, punto de control, etc.</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Cocina Aire Freidora Recetas Apk.md

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-<br />
-<h1>Cocina de aire freidora recetas Apk: Cómo cocinar deliciosas comidas con menos aceite</h1>
- <p>Si te gustan los alimentos fritos pero quieres reducir el aceite y las calorías, es posible que quieras probar una freidora. Una freidora de aire es un aparato de cocina que cocina alimentos circulando aire caliente a su alrededor, creando un exterior crujiente y dorado con un mínimo o ningún aceite. Es una gran manera de disfrutar de sus comidas favoritas sin sentirse culpable o comprometer el sabor. </p>
-<h2>cocina aire freidora recetas apk</h2><br /><p><b><b>Download Zip</b> &bull;&bull;&bull; <a href="https://bltlly.com/2v6Lv6">https://bltlly.com/2v6Lv6</a></b></p><br /><br />
- <h2>Beneficios de freír aire</h2>
- <p>Hay muchas razones por las que es posible que desee utilizar una freidora de aire en lugar de una freidora profunda o un horno. Estos son algunos de los beneficios de la fritura de aire:</p>
- <ul>
-<li><strong>Salud:</strong> La fritura de aire reduce la cantidad de grasa y calorías en los alimentos, así como los niveles de acrilamida, una sustancia química potencialmente dañina que se forma cuando los alimentos con almidón se cocinan a altas temperaturas. La fritura de aire también puede preservar algunos nutrientes que se pierden en otros métodos de cocción. </li>
-<li><strong>Conveniencia:</strong> La fritura de aire es rápida y fácil, ya que precalienta rápidamente y cocina los alimentos de manera uniforme. No necesitas usar mucho aceite o grasa, lo que significa menos desorden y una limpieza más fácil. Tampoco tiene que preocuparse por salpicaduras de aceite caliente o por prenderse fuego. </li>
-<li><strong>Versatilidad:</strong> Freír al aire libre puede cocinar una amplia variedad de alimentos, desde papas fritas congeladas y nuggets de pollo hasta verduras frescas y pescado. También puede hornear, asar, asar y deshidratar alimentos en una freidora. Incluso puede hacer postres como rosquillas, galletas y pasteles. </li>
-</ul>
- <h2>Cómo usar una freidora de aire</h2>
- <p>Para obtener los mejores resultados de tu freidora de aire, necesitas seguir algunos consejos y trucos. Estos son algunos de ellos:</p>
- <ul>
-<li><strong>Precalentar:</strong> La mayoría de las freidoras de aire necesitan precalentar durante unos minutos antes de agregar la comida. Esto asegura que su comida comience a cocinar de inmediato y se vuelva crujiente. </li>
-
-<li><strong>Agitar o voltear:</strong> Para ayudar a la comida crujiente, es necesario agitar o voltear a la mitad del tiempo de cocción. Esto evita que los alimentos se peguen a la cesta y garantiza un dorado uniforme. </li>
-<li><strong>Rocía ligeramente:</strong> Si quieres que tu comida tenga un color dorado y una textura crujiente, puedes rociarla ligeramente con aceite de cocina antes o durante la cocción. Esto también ayuda a evitar que la comida se seque. Sin embargo, no use demasiado aceite, ya que puede gotear en el cajón y causar humo. </li>
-</ul>
- <h2>Recetas de cocina de aire freidora Apk</h2>
- <p>Si usted está buscando un poco de inspiración para sus comidas de aire freidora, es posible que desee echa un vistazo a Cocina Aire Freidora Recetas Apk. Esta es una aplicación gratuita que ofrece cientos de recetas para freír al aire libre, desde aperitivos y aperitivos hasta platos principales y postres. Puedes navegar por categoría, cocina o ingrediente, o buscar recetas específicas. También puedes guardar tus recetas favoritas, calificarlas y compartirlas con tus amigos. </p>
- <p>Para descargar Cocina Freidora Recetas Apk, es necesario seguir estos pasos:</p>
- <ol>
-<li>Ir a [este enlace]( 1 ) en su dispositivo Android. </li>
-<li>Toque en "Descargar APK" y esperar a que el archivo para descargar. </li>
-<li>Abra el archivo y toque en "Instalar". Es posible que necesite permitir la instalación desde fuentes desconocidas en su configuración. </li>
-<li>Una vez instalada la aplicación, ¡ábrela y disfruta! </li>
-</ol>
- <h2>Algunos ejemplos de recetas de la aplicación</h2>
- <p>Para darle una idea de lo que se puede cocinar con Cocina Freidora Recetas Apk, aquí hay algunos ejemplos de recetas de la aplicación:</p>
- <tabla>
-<tr>
-<th>Categoría</th>
-<th>Receta</th>
-<th>Tiempo de cocción</th>
-</tr>
-<tr>
-<td>Aperitivos</td>
-<td>Patatas fritas de freidora de aire</td>
-<td>40 minutos</td>
-</tr>
-<tr>
-<td>Aperitivos</td>
-<td>Espárragos de freidora de aire</td>
-<td>20 minutos</td>
-</tr>
-<tr>
-<td>Platos principales</td>
-<td>Chuletas de cerdo de freidora de aire</td>
-<td>20 minutos</td>
-</tr>
-<tr>
-<td>Platos principales</td>
-<td>Pizza de freidora de aire</td>
-<td>10 minutos</td>
-</tr>
-
-<td>Postres</td>
-<td>Aire freidora Mini pastel de chocolate oscuro</td>
-<td>25 minutos</td>
-</tr>
-<tr>
-<td>Postres</td>
-<td>Cruasanes de queso crema de cereza con freidora de aire</td>
-<td>15 minutos</td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
- <p>Freír al aire es una forma maravillosa de cocinar comidas deliciosas con menos aceite y más sabor. Puedes hacer casi cualquier cosa en una freidora, desde bocadillos crujientes y carnes jugosas hasta verduras tiernas y postres decadentes. Con Kitchen Air Fryer Recipes Apk, se puede acceder a cientos de recetas de fritura de aire, todo de forma gratuita. Puede descargar la aplicación desde [este enlace]( 1 ) y comenzar a cocinar de inmediato. Si eres nuevo en el aire fritura o un profesional experimentado, usted encontrará algo para amar en esta aplicación. Pruébelo hoy y ver por ti mismo! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas y respuestas comunes sobre fritura de aire y cocina Recetas de freidora Apk:</p>
- <ol>
-<li><strong>¿Qué tamaño de freidora de aire necesito? </strong><br>El tamaño de la freidora de aire depende de la cantidad de comida que desea cocinar a la vez y de cuánto espacio tiene en su cocina. Generalmente, una freidora de aire de 3 a 5 cuartos puede acomodar suficiente comida para dos a cuatro personas, mientras que una freidora de aire de 6 a 10 cuartos puede acomodar suficiente comida para cuatro a ocho personas. </li>
- <li><strong>¿Cuáles son algunas de las mejores marcas de freidoras de aire? </strong><br>Hay muchas marcas de freidoras de aire en el mercado, cada una con sus propias características y ventajas. Algunas de las marcas más populares y altamente calificadas son Philips, Ninja, Cosori, Instant Pot y Cuisinart.</li>
- <li><strong>¿Cómo limpio mi freidora de aire? </strong><br>Para limpiar tu freidora de aire, necesitas desenchufarla y dejar que se enfríe completamente. Luego, puede retirar la cesta y el cajón y lavarlos con agua tibia y jabón o en el lavavajillas. Puede limpiar el interior y el exterior de la freidora de aire con un paño húmedo o una esponja. También puede utilizar un cepillo suave o un palillo de dientes para eliminar cualquier residuo de comida del elemento calefactor. </li>
-
- <li><strong>¿Puedo enviar mis propias recetas a Cocina Freidora Recetas Apk? </strong><br>Sí, puede enviar sus propias recetas a Cocina Freidora Recetas Apk mediante el botón "Enviar receta" en la aplicación. También puedes calificar y revisar otras recetas, así como compartirlas con tus amigos en las redes sociales. </li>
-</ol></p>
-<p></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Amantes Y Mejores Amigos Azana.md

@@ -1,68 +0,0 @@
-
-<h1>Descargar Amantes y Mejores Amigos por Azana</h1>
-<p>Si usted está buscando una canción conmovedora y romántica para añadir a su lista de reproducción, es posible que desee echa un vistazo a "Amantes y mejores amigos" por Azana. Azana es una cantante y compositora sudafricana que ha cautivado a muchos oyentes con su mezcla de afro-pop, afro-house vocal y música soul. "Lovers and Best Friends" es una de sus canciones populares de su álbum debut Ingoma, que fue lanzado en 2020. La canción cuenta con Disciples of House, un dúo de talentosos productores que han trabajado con muchos artistas sudafricanos. </p>
-<h2>descargar amantes y mejores amigos azana</h2><br /><p><b><b>Download File</b> ::: <a href="https://bltlly.com/2v6IJX">https://bltlly.com/2v6IJX</a></b></p><br /><br />
-<p>En este artículo, te contaremos más sobre Azana, su carrera musical, y el significado y mensaje de "Amantes y Mejores Amigos". También le mostraremos cómo descargar la canción legalmente y apoyar al artista. Si eres fan de Azana o simplemente tienes curiosidad por su música, sigue leyendo para saber más. </p>
- <h2>Biografía y carrera musical de Azana</h2>
-<p>El verdadero nombre de Azana es Makhosazana Masongo. Nació el 13 de septiembre de 2000, en Chesterville, Durban. Actualmente estudia derecho en la Universidad del Estado Libre. Descubrió su pasión por la música a una edad temprana y comenzó a cantar en los coros de la escuela y la iglesia. También admiraba a artistas como Beyoncé, Nina Simone, Camagwini, Simphiwe Dana y Letta Mbulu.</p>
-<p>Su carrera musical despegó cuando firmó un contrato discográfico con Big City Dreams en 2019. Lanzó su primer single "Your Love" en mayo de 2020, que fue producido por Taffy Da Don. La canción fue un gran éxito y fue certificada doble platino por la Industria Discográfica de Sudáfrica (RiSA). Su álbum debut Ingoma siguió en julio de 2020. El álbum alcanzó el número uno en Apple Music Pop Chart y contó con artistas como Afriikan Papi, Disciples of House y Sun-El Musician.</p>
-
-<p>Azana ha recibido reconocimiento y aclamación por su música. Fue nominada al Mejor Álbum de Pop Afro y Recién Llegado del Año en el 27º South African Music Awards (SAMAs) en 2021. También ganó el premio a la Mejor Artista Femenina en los Mzansi Kwaito & House Music Awards (MKHMA) en 2021. </p>
-<p></p>
- <h2>El significado y mensaje de "Amantes y Mejores Amigos"</h2>
-<p>"Amantes y Mejores Amigos" es una canción hermosa y sincera que celebra el vínculo entre dos personas que no solo son amantes sino también mejores amigos. La canción expresa la alegría y la gratitud de encontrar a alguien que te entiende, te apoya y te aprecia. La canción también reconoce los desafíos y luchas que vienen con cualquier relación, pero afirma el compromiso y la lealtad de los socios. </p>
-<p>La letra de la canción es simple pero potente. Azana canta tanto en inglés como en zulú, creando un contraste y armonía entre los idiomas. Canta en el estribillo: "Tú eres mi amante y mi mejor amigo/ Tú eres mi todo/ Te amo más de lo que las palabras pueden decir/ Tú eres mi amante y mi mejor amigo/ Tú eres mi todo/ Nunca te dejaré ir". She also sings in Zulu: "Ngifuna wena wedwa/ Ngifuna wena wedwa/ Ngifuna wena wedwa/ Ngifuna wena wedwa" which means "I want you only/ I want you only/ I want you only/ I want you only". </p>
-<p>La producción y el género de la canción están influenciados por Afro-house, un subgénero de música house que se originó en Sudáfrica. La canción tiene un ritmo pegadizo y optimista, con una mezcla de ritmos electrónicos, acordes de piano y percusión. La canción también cuenta con las voces de Disciples of House, que añaden una capa de armonía y profundidad a la canción. La canción es adecuada para bailar, relajarse o simplemente disfrutar de la música. </p>
-
- <h2>Las mejores maneras de descargar y transmitir "Amantes y mejores amigos"</h2>
-<p>Si quieres descargar o transmitir "Lovers and Best Friends" de Azana, tienes muchas opciones para elegir. La canción está disponible en varias plataformas y servicios que ofrecen formas legales y éticas para acceder a la música. Estas son algunas de las mejores maneras de descargar o transmitir la canción:</p>
-<tabla>
-<tr>
-<th>Plataforma o servicio</th>
-<th>Características y beneficios</th>
-</tr>
-<tr>
-<td>Música de Apple</td>
-<td>- Ofrece descargas ilimitadas y transmisiones de más de 75 millones de canciones, incluyendo "Amantes y Mejores Amigos" por Azana.<br>- Soporta la escucha sin conexión en múltiples dispositivos. <br>- Proporciona recomendaciones personalizadas, listas de reproducción, estaciones de radio y podcasts. <br>- Cuesta $9.99 por mes para los individuos, $14.99 por mes para las familias, o $4.99 por mes para los estudiantes. <br>- Ofrece una prueba gratuita durante tres meses. </td>
-</tr>
-<tr>
-<td>Spotify</td>
-<td>- Ofrece transmisiones ilimitadas de más de 70 millones de canciones, incluyendo "Amantes y mejores amigos" por Azana.<br>- Permite descargas de hasta 10.000 canciones por dispositivo para usuarios premium. <br>- Proporciona recomendaciones personalizadas, listas de reproducción, estaciones de radio, podcasts y videos. <br>- Cuesta $9.99 por mes para los individuos, $14.99 por mes para las familias, o $4.99 por mes para los estudiantes. <br>- Ofrece una versión gratuita con anuncios y características limitadas. </td>
-</tr>
-<tr>
-<td>Música de YouTube</td>
-<td>- Ofrece transmisiones ilimitadas de más de 60 millones de canciones, incluyendo "Amantes y mejores amigos" por Azana.<br>- Permite descargas de hasta 100.000 canciones por dispositivo para usuarios premium. <br>- Proporciona recomendaciones personalizadas, listas de reproducción, estaciones de radio, podcasts y videos. <br>- Cuesta $9.99 por mes para individuos o $14.99 por mes para familias. <br>- Ofrece una versión gratuita con anuncios y características limitadas. </td>
-</tr>
-<tr>
-<td>Deezer</td>
-
-</ </tr>
-</tabla>
-<p>Como puedes ver, hay muchos beneficios de descargar o transmitir "Amantes y Mejores Amigos" por Azana legal y éticamente. Usted puede disfrutar de la canción en alta calidad, apoyar al artista y la industria de la música, y descubrir más música que le gustaría. También puede evitar los riesgos de descarga ilegal, como virus, malware, demandas o multas. </p>
-<p>Sin embargo, si prefieres no descargar o transmitir la canción, también puedes comprar el CD o vinilo de Ingoma by Azana, que incluye "Lovers and Best Friends" y otras canciones. Puede encontrar el CD o vinilo en línea o en tiendas físicas. Comprar el CD o vinilo también puede darte una copia física de las ilustraciones, letras y créditos del álbum. También puedes apoyar al artista comprando su mercancía, como camisetas, sudaderas, gorras o carteles. </p>
- <h2>Conclusión</h2>
-<p>En conclusión, "Lovers and Best Friends" de Azana es una maravillosa canción que celebra el amor y la amistad entre dos personas. Azana es una talentosa y prometedora cantante y compositora que ha impresionado a muchos fans y críticos con su álbum debut Ingoma. También ha colaborado con muchos otros artistas, como Sun-El Musician y Disciples of House. Si quieres descargar o transmitir "Lovers and Best Friends" de Azana, tienes muchas opciones para elegir. Puedes usar plataformas o servicios como Apple Music, Spotify, YouTube Music o Deezer. También puede comprar el CD o vinilo de Ingoma por Azana o su mercancía. Al hacerlo, puedes apoyar al artista y a la industria de la música, y disfrutar de la canción en alta calidad. </p>
-<p>Esperamos que hayas disfrutado este artículo y hayas aprendido algo nuevo sobre Azana y su música. Si te ha gustado "Lovers and Best Friends" de Azana, puede que también te gusten otras canciones de ella o de artistas similares. Algunas de nuestras recomendaciones son:</p>
-<ul>
-<li>"Uhuru" de Sun-El Músico feat. Azana</li>
-<li>"Mamela" de Mi Casa feat. Azana</li>
-<li>"Uzobuya" de Sun-El Músico feat. Azana</li>
-<li>"Tu amor" por Azana</li>
-
-<li>"Okhokho Bethu" de Vico Da Sporo feat. Azana</li>
-<li>"Jerusalema" por Master KG feat. Nomcebo Zikode</li>
-<li>"Busca tu vida" por Prince Kaybee feat. Msaki</li>
-<li>"Banomoya" de Prince Kaybee feat. Busiswa y TNS</li>
-<li>"Drive" de Black Coffee feat. David Guetta y Delilah Montagu</li>
-</ul>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas y respuestas frecuentes relacionadas con el tema:</p>
-<ol>
-<li><b>¿Cuál es el género de "Amantes y Mejores Amigos" de Azana? </b><br>El género de "Amantes y Mejores Amigos" de Azana es Afro-house, un subgénero de música house que se originó en Sudáfrica.</li>
-<li><b>¿Quiénes son los artistas destacados en "Lovers and Best Friends" de Azana? </b><br>Los artistas destacados en "Lovers and Best Friends" de Azana son Disciples of House, un dúo de productores que han trabajado con muchos artistas sudafricanos. </li>
-<li><b>¿Cuándo se lanzó "Lovers and Best Friends" de Azana? </b><br>"Lovers and Best Friends" de Azana fue lanzado el 17 de julio de 2020, como parte de su álbum debut Ingoma.</li>
-<li><b>¿Cómo puedo descargar o transmitir "Amantes y mejores amigos" por Azana legal y éticamente? </b><br>Puedes descargar o transmitir "Amantes y Mejores Amigos" por Azana legal y éticamente usando plataformas o servicios como Apple Music, Spotify, YouTube Music o Deezer. También puedes comprar el CD o vinilo de Ingoma de Azana o su mercancía. </li>
-<li><b>¿Cuáles son algunas otras canciones de Azana o artistas similares que me podrían gustar? </b><br>Algunas otras canciones de Azana o artistas similares que te pueden gustar son: "Uhuru" de Sun-El Musician feat. Azana, "Mamela" de Mi Casa feat. Azana, "Uzobuya" de Sun-El Musician feat. Azana, "Your Love" de Azana, "Ngize Ngifike" de Sun-El Musician feat. Azana, "Okhokho Bethu" de Vico Da Sporo feat. Azana, "Jerusalema" de Master KG feat. Nomce bo Zikode, "Fetch Your Life" de Prince Kaybee feat. Msaki, "Banomoya" de Prince Kaybee feat. Busiswa y TNS, y "Drive" de Black Coffee feat. David Guetta y Delilah Montagu.</li>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Bishan/Speech_To_Text_Hindi/app.py

@@ -1,83 +0,0 @@
-import soundfile as sf
-import torch
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor,Wav2Vec2ProcessorWithLM
-import gradio as gr
-import sox
-import subprocess
-import time
-
-
-def read_file_and_process(wav_file):
-    filename = wav_file.split('.')[0]
-    filename_16k = filename + "16k.wav"
-    resampler(wav_file, filename_16k)
-    speech, _ = sf.read(filename_16k)
-    print("---------------------------------------------------------")
-    print(speech)
-    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
-    print("---------------------------------------------------------")
-    print(inputs)
-    
-    return inputs
-
-
-def resampler(input_file_path, output_file_path):
-    command = (
-        f"ffmpeg -hide_banner -loglevel panic -i {input_file_path} -ar 16000 -ac 1 -bits_per_raw_sample 16 -vn "
-        f"{output_file_path}"
-    )
-    subprocess.call(command, shell=True)
-
-
-def parse_transcription_with_lm(logits):
-    result = processor_with_LM.batch_decode(logits.cpu().numpy())
-    text = result.text
-    transcription = text[0].replace('<s>','')
-    return transcription
-
-def parse_transcription(logits):
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
-    return transcription
-
-def parse(wav_file, applyLM):
-    
-    # record start time
-    start = time.time()
-    input_values = read_file_and_process(wav_file)
-    with torch.no_grad():
-        logits = model(**input_values).logits
-   
-    # if applyLM:
-    #     return parse_transcription_with_lm(logits)
-    # else:
-    #     return parse_transcription(logits)
-        
-    output = parse_transcription(logits)
-    # record end time
-    end = time.time()
-    print("------------------------------------------------------------------------------------------")
-    print("The time of execution of above program is :",(end-start) * 10**3, "ms")
-    # total time taken
-    print("Execution time of the program is- ", end-start)
-    print("------------------------------------------------------------------------------------------")
-    return output
-
-    
-model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200"
-processor = Wav2Vec2Processor.from_pretrained(model_id)
-processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
-model = Wav2Vec2ForCTC.from_pretrained(model_id)
-
-    
-input_ = gr.Audio(source="upload", type="filepath") 
-txtbox = gr.Textbox(
-            label="Output from model will appear here:",
-            lines=5
-        )
-chkbox = gr.Checkbox(label="Apply LM", value=False)
-
-
-gr.Interface(parse, inputs = [input_, chkbox],  outputs=txtbox,
-             streaming=True, interactive=True,
-             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);

spaces/Buatong/Computing/app.py

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/tests/test_model_e2e.py

@@ -1,43 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-
-import unittest
-import torch
-
-from detectron2.structures import BitMasks, Boxes, Instances
-
-from .common import get_model
-
-
-# TODO(plabatut): Modularize detectron2 tests and re-use
-def make_model_inputs(image, instances=None):
-    if instances is None:
-        return {"image": image}
-
-    return {"image": image, "instances": instances}
-
-
-def make_empty_instances(h, w):
-    instances = Instances((h, w))
-    instances.gt_boxes = Boxes(torch.rand(0, 4))
-    instances.gt_classes = torch.tensor([]).to(dtype=torch.int64)
-    instances.gt_masks = BitMasks(torch.rand(0, h, w))
-    return instances
-
-
-class ModelE2ETest(unittest.TestCase):
-    CONFIG_PATH = ""
-
-    def setUp(self):
-        self.model = get_model(self.CONFIG_PATH)
-
-    def _test_eval(self, sizes):
-        inputs = [make_model_inputs(torch.rand(3, size[0], size[1])) for size in sizes]
-        self.model.eval()
-        self.model(inputs)
-
-
-class DensePoseRCNNE2ETest(ModelE2ETest):
-    CONFIG_PATH = "densepose_rcnn_R_101_FPN_s1x.yaml"
-
-    def test_empty_data(self):
-        self._test_eval([(200, 250), (200, 249)])

spaces/CVPR/LIVE/pybind11/include/pybind11/options.h

@@ -1,65 +0,0 @@
-/*
-    pybind11/options.h: global settings that are configurable at runtime.
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "detail/common.h"
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-
-class options {
-public:
-
-    // Default RAII constructor, which leaves settings as they currently are.
-    options() : previous_state(global_state()) {}
-
-    // Class is non-copyable.
-    options(const options&) = delete;
-    options& operator=(const options&) = delete;
-
-    // Destructor, which restores settings that were in effect before.
-    ~options() {
-        global_state() = previous_state;
-    }
-
-    // Setter methods (affect the global state):
-
-    options& disable_user_defined_docstrings() & { global_state().show_user_defined_docstrings = false; return *this; }
-
-    options& enable_user_defined_docstrings() & { global_state().show_user_defined_docstrings = true; return *this; }
-
-    options& disable_function_signatures() & { global_state().show_function_signatures = false; return *this; }
-
-    options& enable_function_signatures() & { global_state().show_function_signatures = true; return *this; }
-
-    // Getter methods (return the global state):
-
-    static bool show_user_defined_docstrings() { return global_state().show_user_defined_docstrings; }
-
-    static bool show_function_signatures() { return global_state().show_function_signatures; }
-
-    // This type is not meant to be allocated on the heap.
-    void* operator new(size_t) = delete;
-
-private:
-
-    struct state {
-        bool show_user_defined_docstrings = true;  //< Include user-supplied texts in docstrings.
-        bool show_function_signatures = true;      //< Include auto-generated function signatures in docstrings.
-    };
-
-    static state &global_state() {
-        static state instance;
-        return instance;
-    }
-
-    state previous_state;
-};
-
-PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)

spaces/CVPR/LIVE/pybind11/tests/test_call_policies.cpp

@@ -1,101 +0,0 @@
-/*
-    tests/test_call_policies.cpp -- keep_alive and call_guard
-
-    Copyright (c) 2016 Wenzel Jakob <[email protected]>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-
-struct CustomGuard {
-    static bool enabled;
-
-    CustomGuard() { enabled = true; }
-    ~CustomGuard() { enabled = false; }
-
-    static const char *report_status() { return enabled ? "guarded" : "unguarded"; }
-};
-bool CustomGuard::enabled = false;
-
-struct DependentGuard {
-    static bool enabled;
-
-    DependentGuard() { enabled = CustomGuard::enabled; }
-    ~DependentGuard() { enabled = false; }
-
-    static const char *report_status() { return enabled ? "guarded" : "unguarded"; }
-};
-bool DependentGuard::enabled = false;
-
-TEST_SUBMODULE(call_policies, m) {
-    // Parent/Child are used in:
-    // test_keep_alive_argument, test_keep_alive_return_value, test_alive_gc_derived,
-    // test_alive_gc_multi_derived, test_return_none, test_keep_alive_constructor
-    class Child {
-    public:
-        Child() { py::print("Allocating child."); }
-        Child(const Child &) = default;
-        Child(Child &&) = default;
-        ~Child() { py::print("Releasing child."); }
-    };
-    py::class_<Child>(m, "Child")
-        .def(py::init<>());
-
-    class Parent {
-    public:
-        Parent() { py::print("Allocating parent."); }
-        Parent(const Parent& parent) = default;
-        ~Parent() { py::print("Releasing parent."); }
-        void addChild(Child *) { }
-        Child *returnChild() { return new Child(); }
-        Child *returnNullChild() { return nullptr; }
-    };
-    py::class_<Parent>(m, "Parent")
-        .def(py::init<>())
-        .def(py::init([](Child *) { return new Parent(); }), py::keep_alive<1, 2>())
-        .def("addChild", &Parent::addChild)
-        .def("addChildKeepAlive", &Parent::addChild, py::keep_alive<1, 2>())
-        .def("returnChild", &Parent::returnChild)
-        .def("returnChildKeepAlive", &Parent::returnChild, py::keep_alive<1, 0>())
-        .def("returnNullChildKeepAliveChild", &Parent::returnNullChild, py::keep_alive<1, 0>())
-        .def("returnNullChildKeepAliveParent", &Parent::returnNullChild, py::keep_alive<0, 1>());
-
-#if !defined(PYPY_VERSION)
-    // test_alive_gc
-    class ParentGC : public Parent {
-    public:
-        using Parent::Parent;
-    };
-    py::class_<ParentGC, Parent>(m, "ParentGC", py::dynamic_attr())
-        .def(py::init<>());
-#endif
-
-    // test_call_guard
-    m.def("unguarded_call", &CustomGuard::report_status);
-    m.def("guarded_call", &CustomGuard::report_status, py::call_guard<CustomGuard>());
-
-    m.def("multiple_guards_correct_order", []() {
-        return CustomGuard::report_status() + std::string(" & ") + DependentGuard::report_status();
-    }, py::call_guard<CustomGuard, DependentGuard>());
-
-    m.def("multiple_guards_wrong_order", []() {
-        return DependentGuard::report_status() + std::string(" & ") + CustomGuard::report_status();
-    }, py::call_guard<DependentGuard, CustomGuard>());
-
-#if defined(WITH_THREAD) && !defined(PYPY_VERSION)
-    // `py::call_guard<py::gil_scoped_release>()` should work in PyPy as well,
-    // but it's unclear how to test it without `PyGILState_GetThisThreadState`.
-    auto report_gil_status = []() {
-        auto is_gil_held = false;
-        if (auto tstate = py::detail::get_thread_state_unchecked())
-            is_gil_held = (tstate == PyGILState_GetThisThreadState());
-
-        return is_gil_held ? "GIL held" : "GIL released";
-    };
-
-    m.def("with_gil", report_gil_status);
-    m.def("without_gil", report_gil_status, py::call_guard<py::gil_scoped_release>());
-#endif
-}

spaces/CVPR/LIVE/thrust/thrust/advance.h

@@ -1,141 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  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.
- */
-
-
-/*! \file advance.h
- *  \brief Advance an iterator by a given distance.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-namespace thrust
-{
-
-/*! \addtogroup iterators
- *  \{
- */
-
-/*! \p advance(i, n) increments the iterator \p i by the distance \p n.
- *  If <tt>n > 0</tt> it is equivalent to executing <tt>++i</tt> \p n
- *  times, and if <tt>n < 0</tt> it is equivalent to executing <tt>--i</tt>
- *  \p n times. If <tt>n == 0</tt>, the call has no effect.
- *
- *  \param i The iterator to be advanced.
- *  \param n The distance by which to advance the iterator.
- *
- *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>.
- *  \tparam Distance is an integral type that is convertible to \p InputIterator's distance type.
- *
- *  \pre \p n shall be negative only for bidirectional and random access iterators.
- *
- *  The following code snippet demonstrates how to use \p advance to increment
- *  an iterator a given number of times.
- *
- *  \code
- *  #include <thrust/advance.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> vec(13);
- *  thrust::device_vector<int>::iterator iter = vec.begin();
- *
- *  thrust::advance(iter, 7);
- *
- *  // iter - vec.begin() == 7
- *  \endcode
- *
- *  \see http://www.sgi.com/tech/stl/advance.html
- */
-template <typename InputIterator, typename Distance>
-__host__ __device__
-void advance(InputIterator& i, Distance n);
-
-/*! \p next(i, n) returns the \p n th successor of the iterator \p i.
- *
- *  \param i An iterator.
- *  \param n The number of elements to advance.
- *
- *  \tparam InputIterator must meet the <a href="https://en.cppreference.com/w/cpp/named_req/InputIterator">InputIterator</a>.
- *
- *  \pre \p n shall be negative only for bidirectional and random access iterators.
- *
- *  The following code snippet demonstrates how to use \p next.
- *
- *  \code
- *  #include <thrust/advance.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> vec(13);
- *  thrust::device_vector<int>::iterator i0 = vec.begin();
- *
- *  auto i1 = thrust::next(i0);
- *
- *  // i0 - vec.begin() == 0
- *  // i1 - vec.begin() == 1
- *  \endcode
- *
- *  \see https://en.cppreference.com/w/cpp/iterator/next
- */
-#if 0 // Doxygen only
-template <typename InputIterator, typename Distance>
-__host__ __device__
-InputIterator next(
-  InputIterator i
-, typename iterator_traits<InputIterator>::difference_type n = 1
-);
-#endif
-
-/*! \p prev(i, n) returns the \p n th predecessor of the iterator \p i.
- *
- *  \param i An iterator.
- *  \param n The number of elements to descend.
- *
- *  \tparam BidirectionalIterator must meet the <a href="https://en.cppreference.com/w/cpp/named_req/BidirectionalIterator">BidirectionalIterator</a>.
- *
- *  The following code snippet demonstrates how to use \p prev.
- *
- *  \code
- *  #include <thrust/advance.h>
- *  #include <thrust/device_vector.h>
- *  ...
- *  thrust::device_vector<int> vec(13);
- *  thrust::device_vector<int>::iterator i0 = vec.end();
- *
- *  auto i1 = thrust::prev(i0);
- *
- *  // vec.end() - i0 == 0
- *  // vec.end() - i1 == 1
- *  \endcode
- *
- *  \see https://en.cppreference.com/w/cpp/iterator/prev
- */
-#if 0 // Doxygen only
-template <typename BidirectionalIterator, typename Distance>
-__host__ __device__
-BidirectionalIterator prev(
-  BidirectionalIterator i
-, typename iterator_traits<BidirectionalIterator>::difference_type n = 1
-);
-#endif
-
-/*! \} // end iterators
- */
-
-} // end thrust
-
-#include <thrust/detail/advance.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/adjacent_difference.h

@@ -1,58 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  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.
- */
-
-
-/*! \file adjacent_difference.h
- *  \brief Generic implementation of adjacent_difference.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy, typename InputIterator, typename OutputIterator>
-__host__ __device__
-OutputIterator adjacent_difference(thrust::execution_policy<DerivedPolicy> &exec,
-                                   InputIterator first, InputIterator last,
-                                   OutputIterator result);
-
-
-template<typename DerivedPolicy, typename InputIterator, typename OutputIterator, typename BinaryFunction>
-__host__ __device__
-OutputIterator adjacent_difference(thrust::execution_policy<DerivedPolicy> &exec,
-                                   InputIterator first, InputIterator last,
-                                   OutputIterator result,
-                                   BinaryFunction binary_op);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/adjacent_difference.inl>
-

spaces/CVPR/regionclip-demo/detectron2/modeling/proposal_generator/rrpn.py

@@ -1,203 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import logging
-from typing import Dict, List
-import torch
-
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec, batched_nms_rotated, cat
-from detectron2.structures import Instances, RotatedBoxes, pairwise_iou_rotated
-from detectron2.utils.memory import retry_if_cuda_oom
-
-from ..box_regression import Box2BoxTransformRotated
-from .build import PROPOSAL_GENERATOR_REGISTRY
-from .rpn import RPN
-
-logger = logging.getLogger(__name__)
-
-
-def find_top_rrpn_proposals(
-    proposals,
-    pred_objectness_logits,
-    image_sizes,
-    nms_thresh,
-    pre_nms_topk,
-    post_nms_topk,
-    min_box_size,
-    training,
-):
-    """
-    For each feature map, select the `pre_nms_topk` highest scoring proposals,
-    apply NMS, clip proposals, and remove small boxes. Return the `post_nms_topk`
-    highest scoring proposals among all the feature maps if `training` is True,
-    otherwise, returns the highest `post_nms_topk` scoring proposals for each
-    feature map.
-
-    Args:
-        proposals (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A, 5).
-            All proposal predictions on the feature maps.
-        pred_objectness_logits (list[Tensor]): A list of L tensors. Tensor i has shape (N, Hi*Wi*A).
-        image_sizes (list[tuple]): sizes (h, w) for each image
-        nms_thresh (float): IoU threshold to use for NMS
-        pre_nms_topk (int): number of top k scoring proposals to keep before applying NMS.
-            When RRPN is run on multiple feature maps (as in FPN) this number is per
-            feature map.
-        post_nms_topk (int): number of top k scoring proposals to keep after applying NMS.
-            When RRPN is run on multiple feature maps (as in FPN) this number is total,
-            over all feature maps.
-        min_box_size(float): minimum proposal box side length in pixels (absolute units wrt
-            input images).
-        training (bool): True if proposals are to be used in training, otherwise False.
-            This arg exists only to support a legacy bug; look for the "NB: Legacy bug ..."
-            comment.
-
-    Returns:
-        proposals (list[Instances]): list of N Instances. The i-th Instances
-            stores post_nms_topk object proposals for image i.
-    """
-    num_images = len(image_sizes)
-    device = proposals[0].device
-
-    # 1. Select top-k anchor for every level and every image
-    topk_scores = []  # #lvl Tensor, each of shape N x topk
-    topk_proposals = []
-    level_ids = []  # #lvl Tensor, each of shape (topk,)
-    batch_idx = torch.arange(num_images, device=device)
-    for level_id, proposals_i, logits_i in zip(
-        itertools.count(), proposals, pred_objectness_logits
-    ):
-        Hi_Wi_A = logits_i.shape[1]
-        num_proposals_i = min(pre_nms_topk, Hi_Wi_A)
-
-        # sort is faster than topk (https://github.com/pytorch/pytorch/issues/22812)
-        # topk_scores_i, topk_idx = logits_i.topk(num_proposals_i, dim=1)
-        logits_i, idx = logits_i.sort(descending=True, dim=1)
-        topk_scores_i = logits_i[batch_idx, :num_proposals_i]
-        topk_idx = idx[batch_idx, :num_proposals_i]
-
-        # each is N x topk
-        topk_proposals_i = proposals_i[batch_idx[:, None], topk_idx]  # N x topk x 5
-
-        topk_proposals.append(topk_proposals_i)
-        topk_scores.append(topk_scores_i)
-        level_ids.append(torch.full((num_proposals_i,), level_id, dtype=torch.int64, device=device))
-
-    # 2. Concat all levels together
-    topk_scores = cat(topk_scores, dim=1)
-    topk_proposals = cat(topk_proposals, dim=1)
-    level_ids = cat(level_ids, dim=0)
-
-    # 3. For each image, run a per-level NMS, and choose topk results.
-    results = []
-    for n, image_size in enumerate(image_sizes):
-        boxes = RotatedBoxes(topk_proposals[n])
-        scores_per_img = topk_scores[n]
-        valid_mask = torch.isfinite(boxes.tensor).all(dim=1) & torch.isfinite(scores_per_img)
-        if not valid_mask.all():
-            boxes = boxes[valid_mask]
-            scores_per_img = scores_per_img[valid_mask]
-        boxes.clip(image_size)
-
-        # filter empty boxes
-        keep = boxes.nonempty(threshold=min_box_size)
-        lvl = level_ids
-        if keep.sum().item() != len(boxes):
-            boxes, scores_per_img, lvl = (boxes[keep], scores_per_img[keep], level_ids[keep])
-
-        keep = batched_nms_rotated(boxes.tensor, scores_per_img, lvl, nms_thresh)
-        # In Detectron1, there was different behavior during training vs. testing.
-        # (https://github.com/facebookresearch/Detectron/issues/459)
-        # During training, topk is over the proposals from *all* images in the training batch.
-        # During testing, it is over the proposals for each image separately.
-        # As a result, the training behavior becomes batch-dependent,
-        # and the configuration "POST_NMS_TOPK_TRAIN" end up relying on the batch size.
-        # This bug is addressed in Detectron2 to make the behavior independent of batch size.
-        keep = keep[:post_nms_topk]
-
-        res = Instances(image_size)
-        res.proposal_boxes = boxes[keep]
-        res.objectness_logits = scores_per_img[keep]
-        results.append(res)
-    return results
-
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class RRPN(RPN):
-    """
-    Rotated Region Proposal Network described in :paper:`RRPN`.
-    """
-
-    @configurable
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        if self.anchor_boundary_thresh >= 0:
-            raise NotImplementedError(
-                "anchor_boundary_thresh is a legacy option not implemented for RRPN."
-            )
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
-        ret = super().from_config(cfg, input_shape)
-        ret["box2box_transform"] = Box2BoxTransformRotated(weights=cfg.MODEL.RPN.BBOX_REG_WEIGHTS)
-        return ret
-
-    @torch.no_grad()
-    def label_and_sample_anchors(self, anchors: List[RotatedBoxes], gt_instances: List[Instances]):
-        """
-        Args:
-            anchors (list[RotatedBoxes]): anchors for each feature map.
-            gt_instances: the ground-truth instances for each image.
-
-        Returns:
-            list[Tensor]:
-                List of #img tensors. i-th element is a vector of labels whose length is
-                the total number of anchors across feature maps. Label values are in {-1, 0, 1},
-                with meanings: -1 = ignore; 0 = negative class; 1 = positive class.
-            list[Tensor]:
-                i-th element is a Nx5 tensor, where N is the total number of anchors across
-                feature maps.  The values are the matched gt boxes for each anchor.
-                Values are undefined for those anchors not labeled as 1.
-        """
-        anchors = RotatedBoxes.cat(anchors)
-
-        gt_boxes = [x.gt_boxes for x in gt_instances]
-        del gt_instances
-
-        gt_labels = []
-        matched_gt_boxes = []
-        for gt_boxes_i in gt_boxes:
-            """
-            gt_boxes_i: ground-truth boxes for i-th image
-            """
-            match_quality_matrix = retry_if_cuda_oom(pairwise_iou_rotated)(gt_boxes_i, anchors)
-            matched_idxs, gt_labels_i = retry_if_cuda_oom(self.anchor_matcher)(match_quality_matrix)
-            # Matching is memory-expensive and may result in CPU tensors. But the result is small
-            gt_labels_i = gt_labels_i.to(device=gt_boxes_i.device)
-
-            # A vector of labels (-1, 0, 1) for each anchor
-            gt_labels_i = self._subsample_labels(gt_labels_i)
-
-            if len(gt_boxes_i) == 0:
-                # These values won't be used anyway since the anchor is labeled as background
-                matched_gt_boxes_i = torch.zeros_like(anchors.tensor)
-            else:
-                # TODO wasted indexing computation for ignored boxes
-                matched_gt_boxes_i = gt_boxes_i[matched_idxs].tensor
-
-            gt_labels.append(gt_labels_i)  # N,AHW
-            matched_gt_boxes.append(matched_gt_boxes_i)
-        return gt_labels, matched_gt_boxes
-
-    @torch.no_grad()
-    def predict_proposals(self, anchors, pred_objectness_logits, pred_anchor_deltas, image_sizes):
-        pred_proposals = self._decode_proposals(anchors, pred_anchor_deltas)
-        return find_top_rrpn_proposals(
-            pred_proposals,
-            pred_objectness_logits,
-            image_sizes,
-            self.nms_thresh,
-            self.pre_nms_topk[self.training],
-            self.post_nms_topk[self.training],
-            self.min_box_size,
-            self.training,
-        )

spaces/CanonOverseer/Canons-Den/Dockerfile

@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
-    apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=production
-CMD [ "npm", "start" ]

spaces/CarlDennis/Lovelive-VITS-JPZH/text/korean.py

@@ -1,205 +0,0 @@
-import re
-from jamo import h2j, j2hcj
-import ko_pron
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('ㄳ', 'ㄱㅅ'),
-    ('ㄵ', 'ㄴㅈ'),
-    ('ㄶ', 'ㄴㅎ'),
-    ('ㄺ', 'ㄹㄱ'),
-    ('ㄻ', 'ㄹㅁ'),
-    ('ㄼ', 'ㄹㅂ'),
-    ('ㄽ', 'ㄹㅅ'),
-    ('ㄾ', 'ㄹㅌ'),
-    ('ㄿ', 'ㄹㅍ'),
-    ('ㅀ', 'ㄹㅎ'),
-    ('ㅄ', 'ㅂㅅ'),
-    ('ㅘ', 'ㅗㅏ'),
-    ('ㅙ', 'ㅗㅐ'),
-    ('ㅚ', 'ㅗㅣ'),
-    ('ㅝ', 'ㅜㅓ'),
-    ('ㅞ', 'ㅜㅔ'),
-    ('ㅟ', 'ㅜㅣ'),
-    ('ㅢ', 'ㅡㅣ'),
-    ('ㅑ', 'ㅣㅏ'),
-    ('ㅒ', 'ㅣㅐ'),
-    ('ㅕ', 'ㅣㅓ'),
-    ('ㅖ', 'ㅣㅔ'),
-    ('ㅛ', 'ㅣㅗ'),
-    ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', '에이'),
-    ('b', '비'),
-    ('c', '시'),
-    ('d', '디'),
-    ('e', '이'),
-    ('f', '에프'),
-    ('g', '지'),
-    ('h', '에이치'),
-    ('i', '아이'),
-    ('j', '제이'),
-    ('k', '케이'),
-    ('l', '엘'),
-    ('m', '엠'),
-    ('n', '엔'),
-    ('o', '오'),
-    ('p', '피'),
-    ('q', '큐'),
-    ('r', '아르'),
-    ('s', '에스'),
-    ('t', '티'),
-    ('u', '유'),
-    ('v', '브이'),
-    ('w', '더블유'),
-    ('x', '엑스'),
-    ('y', '와이'),
-    ('z', '제트')
-]]
-
-# List of (ipa, lazy ipa) pairs:
-_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('t͡ɕ','ʧ'),
-    ('d͡ʑ','ʥ'),
-    ('ɲ','n^'),
-    ('ɕ','ʃ'),
-    ('ʷ','w'),
-    ('ɭ','l`'),
-    ('ʎ','ɾ'),
-    ('ɣ','ŋ'),
-    ('ɰ','ɯ'),
-    ('ʝ','j'),
-    ('ʌ','ə'),
-    ('ɡ','g'),
-    ('\u031a','#'),
-    ('\u0348','='),
-    ('\u031e',''),
-    ('\u0320',''),
-    ('\u0339','')
-]]
-
-
-def latin_to_hangul(text):
-    for regex, replacement in _latin_to_hangul:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def divide_hangul(text):
-    text = j2hcj(h2j(text))
-    for regex, replacement in _hangul_divided:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def hangul_number(num, sino=True):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    num = re.sub(',', '', num)
-
-    if num == '0':
-        return '영'
-    if not sino and num == '20':
-        return '스무'
-
-    digits = '123456789'
-    names = '일이삼사오육칠팔구'
-    digit2name = {d: n for d, n in zip(digits, names)}
-
-    modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-    decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-    digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-    digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-    spelledout = []
-    for i, digit in enumerate(num):
-        i = len(num) - i - 1
-        if sino:
-            if i == 0:
-                name = digit2name.get(digit, '')
-            elif i == 1:
-                name = digit2name.get(digit, '') + '십'
-                name = name.replace('일십', '십')
-        else:
-            if i == 0:
-                name = digit2mod.get(digit, '')
-            elif i == 1:
-                name = digit2dec.get(digit, '')
-        if digit == '0':
-            if i % 4 == 0:
-                last_three = spelledout[-min(3, len(spelledout)):]
-                if ''.join(last_three) == '':
-                    spelledout.append('')
-                    continue
-            else:
-                spelledout.append('')
-                continue
-        if i == 2:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 3:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 4:
-            name = digit2name.get(digit, '') + '만'
-            name = name.replace('일만', '만')
-        elif i == 5:
-            name = digit2name.get(digit, '') + '십'
-            name = name.replace('일십', '십')
-        elif i == 6:
-            name = digit2name.get(digit, '') + '백'
-            name = name.replace('일백', '백')
-        elif i == 7:
-            name = digit2name.get(digit, '') + '천'
-            name = name.replace('일천', '천')
-        elif i == 8:
-            name = digit2name.get(digit, '') + '억'
-        elif i == 9:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 10:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 11:
-            name = digit2name.get(digit, '') + '천'
-        elif i == 12:
-            name = digit2name.get(digit, '') + '조'
-        elif i == 13:
-            name = digit2name.get(digit, '') + '십'
-        elif i == 14:
-            name = digit2name.get(digit, '') + '백'
-        elif i == 15:
-            name = digit2name.get(digit, '') + '천'
-        spelledout.append(name)
-    return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-    '''Reference https://github.com/Kyubyong/g2pK'''
-    tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-    for token in tokens:
-        num, classifier = token
-        if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-            spelledout = hangul_number(num, sino=False)
-        else:
-            spelledout = hangul_number(num, sino=True)
-        text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-    # digit by digit for remaining digits
-    digits = '0123456789'
-    names = '영일이삼사오육칠팔구'
-    for d, n in zip(digits, names):
-        text = text.replace(d, n)
-    return text
-
-
-def korean_to_lazy_ipa(text):
-    text = latin_to_hangul(text)
-    text = number_to_hangul(text)
-    text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa'),text).split('] ~ [')[0]
-    for regex, replacement in _ipa_to_lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text

spaces/ChandraMohanNayal/AutoGPT/autogpt/__main__.py

@@ -1,5 +0,0 @@
-"""Auto-GPT: A GPT powered AI Assistant"""
-import autogpt.cli
-
-if __name__ == "__main__":
-    autogpt.cli.main()

spaces/ChrisCaviar/ControlNet-v1-1/app_segmentation.py

@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(label='Preprocessor',
-                                                 choices=['UPerNet', 'None'],
-                                                 type='value',
-                                                 value='UPerNet')
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='segmentation',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='segmentation')
-    demo = create_demo(model.process_segmentation)
-    demo.queue().launch()

spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/model/red/tool.js

@@ -1,428 +0,0 @@
-import fs from 'fs'
-import { createHash, randomUUID } from 'crypto'
-import { resolve, join, dirname, basename } from 'path'
-import fetch, { FormData, Blob } from 'node-fetch'
-import { fileURLToPath } from 'url'
-import { exec, spawn } from 'child_process'
-import os from 'os'
-import _ from 'lodash'
-import { Stream } from "stream"
-import YAML from 'yaml'
-import { TMP_DIR } from '../tool.js'
-
-const user = os.userInfo().username
-let redPath = `C:/Users/${user}/.chronocat`
-if (!fs.existsSync(redPath)) {
-    redPath = `C:/Users/${user}/AppData/Roaming/BetterUniverse/QQNT`
-}
-
-const roleMap = {
-    2: 'member',
-    3: 'admin',
-    4: 'owner'
-}
-
-async function uploadImg(bot, msg) {
-    const file = await upload(bot, msg, 'image/png')
-    if (!file.imageInfo) throw "获取图片信息失败,请检查图片状态"
-    return {
-        elementType: 2,
-        picElement: {
-            md5HexStr: file.md5,
-            fileSize: file.fileSize,
-            picHeight: file.imageInfo.height,
-            picWidth: file.imageInfo.width,
-            fileName: basename(file.ntFilePath),
-            sourcePath: file.ntFilePath,
-            picType: file.imageInfo.type === 'gif' ? 2000 : 1000
-        }
-    }
-}
-
-async function upload(bot, msg, contentType) {
-    if (!msg) throw { noLog: true }
-    let buffer
-    if (msg instanceof Stream.Readable) {
-        buffer = fs.readFileSync(msg.path)
-        contentType = contentType.split('/')[0] + '/' + msg.path.substring(msg.path.lastIndexOf('.') + 1)
-    } if (Buffer.isBuffer(msg)) {
-        buffer = msg
-    } else if (msg.match(/^base64:\/\//)) {
-        buffer = Buffer.from(msg.replace(/^base64:\/\//, ""), 'base64')
-    } else if (msg.startsWith('http')) {
-        const img = await fetch(msg)
-        const type = img.headers.get('content-type');
-        if (type) contentType = type
-        const arrayBuffer = await img.arrayBuffer()
-        buffer = Buffer.from(arrayBuffer)
-    } else if (msg.startsWith('file://')) {
-        buffer = fs.readFileSync(msg.replace(/file:\/{2,3}/, ''))
-        contentType = contentType.split('/')[0] + '/' + msg.substring(msg.lastIndexOf('.') + 1)
-    } else {
-        buffer = fs.readFileSync(msg)
-        contentType = contentType.split('/')[0] + '/' + msg.substring(msg.lastIndexOf('.') + 1)
-    }
-    const blob = new Blob([buffer], { type: contentType })
-    const formData = new FormData()
-    formData.append('file', blob, 'ws-plugin.' + contentType.split('/')[1])
-    const file = await bot.sendApi('POST', 'upload', formData)
-    if (file.error) {
-        throw file.error
-    }
-    file.contentType = contentType
-    return file
-}
-
-async function uploadAudio(file) {
-    let buffer
-    if (file.match(/^base64:\/\//)) {
-        buffer = Buffer.from(file.replace(/^base64:\/\//, ""), 'base64')
-    } else if (file.startsWith('http')) {
-        const http = await fetch(file)
-        const arrayBuffer = await http.arrayBuffer()
-        buffer = Buffer.from(arrayBuffer)
-    } else if (file.startsWith('file://')) {
-        buffer = fs.readFileSync(file.replace(/file:\/{2,3}/, ''))
-    }
-    const head = buffer.subarray(0, 7).toString()
-    let filePath
-    let duration = 0
-    if (!head.includes('SILK')) {
-        const tmpPath = await saveTmp(buffer)
-        duration = await getDuration(tmpPath)
-        const res = await audioTrans(tmpPath)
-        filePath = res.silkFile
-        buffer = fs.readFileSync(filePath)
-    } else {
-        filePath = await saveTmp(buffer)
-    }
-
-    const hash = createHash('md5')
-    hash.update(buffer.toString('binary'), 'binary')
-    const md5 = hash.digest('hex')
-    return {
-        elementType: 4,
-        pttElement: {
-            md5HexStr: md5,
-            fileSize: buffer.length,
-            fileName: md5 + '.amr',
-            filePath: filePath,
-            // waveAmplitudes: [36, 28, 68, 28, 84, 28],
-            waveAmplitudes: [
-                99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99
-            ],
-            duration: duration
-        }
-    }
-}
-
-function audioTrans(tmpPath, samplingRate = '24000') {
-    return new Promise((resolve, reject) => {
-        const pcmFile = join(TMP_DIR, randomUUID({ disableEntropyCache: true }))
-        exec(`ffmpeg -y -i "${tmpPath}" -ar ${samplingRate} -ac 1 -f s16le "${pcmFile}"`, async () => {
-            fs.unlink(tmpPath, () => { })
-            fs.access(pcmFile, fs.constants.F_OK, (err) => {
-                if (err) {
-                    reject('音频转码失败, 请确保你的 ffmpeg 已正确安装')
-                }
-            })
-
-            const silkFile = join(TMP_DIR, randomUUID({ disableEntropyCache: true }))
-            try {
-                await pcmToSilk(pcmFile, silkFile, samplingRate)
-            } catch (error) {
-                reject('red发送语音暂不支持非win系统')
-            }
-            fs.unlink(pcmFile, () => { })
-
-            resolve({
-                silkFile
-            })
-        })
-    })
-}
-
-function pcmToSilk(input, output, samplingRate) {
-    return new Promise((resolve, reject) => {
-        const args = ['-i', input, '-s', samplingRate, '-o', output]
-        const __filename = fileURLToPath(import.meta.url);
-        const __dirname = dirname(__filename);
-        const child = spawn(join(__dirname, './cli.exe'), args)
-        child.on('exit', () => {
-            fs.access(output, fs.constants.F_OK, (err) => {
-                if (err) {
-                    reject('音频转码失败')
-                }
-            })
-            // fs.stat(output, (err, stats) => {
-            //     if (err) {
-            //         console.error(err);
-            //         return;
-            //     }
-            //     fs.truncate(output, stats.size - 1, err => {
-            //         if (err) {
-            //             console.error(err);
-            //             return;
-            //         }
-            //     });
-            // });
-            resolve()
-        })
-    })
-}
-
-function getDuration(file) {
-    return new Promise((resolve, reject) => {
-        exec(`ffmpeg -i ${file}`, function (err, stdout, stderr) {
-            const outStr = stderr.toString()
-            const regDuration = /Duration\: ([0-9\:\.]+),/
-            const rs = regDuration.exec(outStr)
-            if (rs === null) {
-                reject("获取音频时长失败, 请确保你的 ffmpeg 已正确安装")
-            } else if (rs[1]) {
-                const time = rs[1]
-                const parts = time.split(":")
-                const seconds = (+parts[0]) * 3600 + (+parts[1]) * 60 + (+parts[2])
-                const round = seconds.toString().split('.')[0]
-                resolve(+ round)
-            }
-        })
-    })
-}
-
-async function saveTmp(data, ext = null) {
-    ext = ext ? '.' + ext : ''
-    const filename = randomUUID({ disableEntropyCache: true }) + ext
-    const tmpPath = resolve(TMP_DIR, filename)
-    fs.writeFileSync(tmpPath, data)
-    return tmpPath
-}
-
-async function getNtPath(bot) {
-    let dataPath
-    try {
-        const buffer = fs.readFileSync('./plugins/ws-plugin/resources/common/cont/logo.png')
-        const blob = new Blob([buffer], { type: 'image/png' })
-        const formData = new FormData()
-        formData.append('file', blob, '1.png')
-        const file = await bot.sendApi('POST', 'upload', formData)
-        fs.unlinkSync(file.ntFilePath)
-        const index = file.ntFilePath.indexOf('nt_data');
-        dataPath = file.ntFilePath.slice(0, index + 'nt_data'.length);
-    } catch (error) {
-        return null
-    }
-    return dataPath
-}
-
-async function uploadVideo(bot, file) {
-    let type = 'mp4'
-    if (file.match(/^base64:\/\//)) {
-        const buffer = Buffer.from(file.replace(/^base64:\/\//, ""), 'base64')
-        file = join(TMP_DIR, randomUUID({ disableEntropyCache: true }) + '.' + type)
-        fs.writeFileSync(file, buffer)
-    } else {
-        file = file.replace(/file:\/{2,3}/, '')
-        type = file.substring(file.lastIndexOf('.') + 1)
-        const Temp = join(TMP_DIR, randomUUID({ disableEntropyCache: true }) + '.' + type)
-        fs.copyFileSync(file, Temp)
-        file = Temp
-    }
-    const ntPath = await getNtPath(bot)
-    if (!ntPath) return
-    const now = new Date();
-    const year = now.getFullYear();
-    const month = now.getMonth() + 1;
-    const date = `${year}-${month.toString().padStart(2, '0')}`;
-    const video = await getVideoInfo(file)
-
-    let oriPath = `${ntPath}/Video`
-    if (!fs.existsSync(oriPath)) fs.mkdirSync(oriPath)
-    oriPath = `${oriPath}/${date}`
-    if (!fs.existsSync(oriPath)) fs.mkdirSync(oriPath)
-    oriPath = `${oriPath}/Ori`
-    if (!fs.existsSync(oriPath)) fs.mkdirSync(oriPath)
-    oriPath = `${oriPath}/${video.videoMd5}.${type}`
-
-    let thumbPath = `${ntPath}/Video/${date}/Thumb`
-    if (!fs.existsSync(thumbPath)) fs.mkdirSync(thumbPath)
-    thumbPath = `${thumbPath}/${video.videoMd5}_0.png`
-
-    fs.copyFileSync(file, oriPath)
-    fs.unlinkSync(file)
-    const thumb = await getThumbInfo(oriPath, thumbPath)
-    return {
-        elementType: 5,
-        videoElement: {
-            filePath: oriPath,
-            fileName: video.videoMd5 + '.' + type,
-            videoMd5: video.videoMd5,
-            thumbMd5: thumb.thumbMd5,
-            fileTime: video.fileTime,
-            thumbSize: thumb.thumbSize,
-            fileSize: video.fileSize,
-            thumbWidth: thumb.thumbWidth,
-            thumbHeight: thumb.thumbHeight
-        }
-    }
-}
-
-async function getVideoInfo(file) {
-    const fileTime = await getVideoTime(file)
-    const videoMd5 = await getVideoMd5(file)
-    const fileSize = fs.readFileSync(file).length
-    return {
-        fileTime,
-        videoMd5,
-        fileSize
-    }
-}
-
-function getVideoMd5(file) {
-    return new Promise((resolve, reject) => {
-        const stream = fs.createReadStream(file);
-        const hash = createHash('md5');
-        stream.on('data', chunk => {
-            hash.update(chunk);
-        });
-        stream.on('end', () => {
-            const md5 = hash.digest('hex');
-            resolve(md5)
-        });
-    })
-}
-
-function getVideoTime(file) {
-    return new Promise((resolve, reject) => {
-        exec(`ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 "${file}"`, (error, stdout, stderr) => {
-            if (error) {
-                reject('获取视频长度失败, 请确保你的 ffmpeg 已正确安装')
-            }
-            const durationInSeconds = parseInt(stdout);
-            resolve(durationInSeconds)
-        });
-    })
-}
-
-async function getThumbInfo(file, thumbPath) {
-
-    const tempPath = join(TMP_DIR, randomUUID({ disableEntropyCache: true }) + '.jpg')
-
-    const { thumbMd5, thumbSize } = await extractThumbnail(file, tempPath);
-
-    const { thumbWidth, thumbHeight } = getImageSize(tempPath);
-
-    fs.copyFileSync(tempPath, thumbPath)
-    fs.unlinkSync(tempPath)
-
-    return { thumbMd5, thumbWidth, thumbHeight, thumbSize };
-}
-
-function extractThumbnail(inputFile, outputFile) {
-    return new Promise((resolve, reject) => {
-        exec(`ffmpeg -i "${inputFile}" -ss 00:00:00.000 -vframes 1 -vf "scale=iw/3:ih/3" "${outputFile}"
-        `, async () => {
-            fs.access(outputFile, fs.constants.F_OK, (err) => {
-                if (err) {
-                    reject('获取视频封面失败, 请确保你的 ffmpeg 已正确安装')
-                }
-            })
-
-            const buffer = fs.readFileSync(outputFile);
-            const hash = createHash('md5');
-            hash.update(buffer);
-            resolve({
-                thumbMd5: hash.digest('hex'),
-                thumbSize: buffer.length
-            })
-        })
-    })
-}
-
-function getImageSize(file) {
-    const buffer = fs.readFileSync(file);
-    const start = buffer.indexOf(Buffer.from([0xff, 0xc0]));
-    const thumbHeight = buffer.readUInt16BE(start + 5);
-    const thumbWidth = buffer.readUInt16BE(start + 7);
-    return { thumbWidth, thumbHeight };
-}
-
-async function uploadFile(file) {
-    let buffer, name, path = process.cwd() + '/plugins/ws-plugin/Temp/'
-    if (file.startsWith('http')) {
-        const http = await fetch(file)
-        const arrayBuffer = await http.arrayBuffer()
-        buffer = Buffer.from(arrayBuffer)
-        name = file.substring(file.lastIndexOf('/') + 1)
-        path = path + name
-        fs.writeFileSync(path, buffer);
-    } else if (file.startsWith('file://')) {
-        buffer = fs.readFileSync(file.replace(/file:\/{2,3}/, ''))
-        name = file.substring(file.lastIndexOf('/') + 1)
-        path = path + name
-        fs.copyFileSync(file, path)
-    } else if (Buffer.isBuffer(file)) {
-        buffer = file
-        name = 'buffer'
-        path = path + name
-        fs.writeFileSync(path, buffer);
-    } else {
-        buffer = fs.readFileSync(file)
-        name = file.substring(file.lastIndexOf('/') + 1)
-        path = path + name
-        fs.copyFileSync(file, path)
-    }
-    const size = buffer.length
-    const hash = createHash('md5');
-    hash.update(buffer);
-    const md5 = hash.digest('hex')
-    return {
-        elementType: 3,
-        fileElement: {
-            fileMd5: md5,
-            fileName: name,
-            filePath: path,
-            fileSize: size,
-        }
-    }
-}
-
-function getToken() {
-    let tokenPath
-    try {
-        if (os.platform() === 'win32') {
-            tokenPath = `${redPath}/config/chronocat.yml`
-            if (fs.existsSync(tokenPath)) {
-                const data = YAML.parse(fs.readFileSync(tokenPath, 'utf-8'))
-                for (const i of data?.servers || []) {
-                    if (i.type === 'red') {
-                        return i.token
-                    }
-                }
-                logger.error('[ws-plugin] 请检查chronocat配置是否开启red服务')
-                return false
-            } else {
-                tokenPath = `${redPath}/RED_PROTOCOL_TOKEN`
-                return fs.readFileSync(tokenPath, 'utf-8')
-            }
-        } else {
-            logger.error('[ws-plugin] 非Windows系统请自行获取Token')
-            return false
-        }
-    } catch (error) {
-        logger.error('[ws-plugin] QQNT自动获取Token失败,请检查是否已安装Chronocat并尝试手动获取')
-        logger.error(error)
-        return false
-    }
-}
-
-export {
-    uploadImg,
-    uploadAudio,
-    uploadVideo,
-    uploadFile,
-    getToken,
-    getNtPath,
-    roleMap,
-    redPath
-}

spaces/CjangCjengh/Sanskrit-TTS/utils.py

@@ -1,75 +0,0 @@
-import logging
-from json import loads
-from torch import load, FloatTensor
-from numpy import float32
-import librosa
-
-
-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__()
-
-
-def load_checkpoint(checkpoint_path, model):
-  checkpoint_dict = load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  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:
-      logging.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)
-  logging.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = loads(data)
-
-  hparams = HParams(**config)
-  return hparams
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
-  audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
-  return FloatTensor(audio.astype(float32))

spaces/CofAI/LengthConverter/style.css

@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}

spaces/CofAI/chat.v1/web.html

@@ -1,60 +0,0 @@
-<!DOCTYPE html>
-<html>
-  <head>
-    <title>API Demo</title>
-  </head>
-  <body>
-    <h1>API Demo</h1>
-    <label for="day">Select a day:</label>
-    <select id="day">
-      <option value="monday">Monday</option>
-      <option value="tuesday">Tuesday</option>
-      <option value="wednesday">Wednesday</option>
-      <option value="thursday">Thursday</option>
-      <option value="friday">Friday</option>
-      <option value="saturday">Saturday</option>
-      <option value="sunday">Sunday</option>
-    </select>
-    <label for="data">Select data:</label>
-    <select id="data" name="data"></select>
-    <br><br>
-    <input type="button" value="Submit" onclick="fetchData()">
-
-    <!-- <br><br>
-    <button onclick="fetchAPI()">Submit</button>
-    <br><br> -->
-    <div id="result"></div>
-    <script>
-     function fetchData() {
-    const day = document.getElementById("day").value;
-    const dataSelect = document.getElementById("data");
-    const resultDiv = document.getElementById("result");
-
-    // Clear previous results
-    resultDiv.innerHTML = "";
-
-    // Make API request
-    fetch(`https://api.example.com/data/${day}`)
-        .then(response => response.json())
-        .then(data => {
-            // Populate data dropdown
-            dataSelect.innerHTML = "";
-            data.forEach(item => {
-                const option = document.createElement("option");
-                option.value = item.value;
-                option.textContent = item.label;
-                dataSelect.appendChild(option);
-            });
-
-            // Show fetched data
-            resultDiv.innerHTML = `Data for ${day}: ${JSON.stringify(data)}`;
-        })
-        .catch(error => {
-            console.error(error);
-            resultDiv.innerHTML = "Error fetching data.";
-        });
-}
-
-    </script>
-  </body>
-</html>

spaces/CofAI/chat/g4f/models.py

@@ -1,233 +0,0 @@
-from g4f import Provider
-
-
-class Model:
-    class model:
-        name: str
-        base_provider: str
-        best_provider: str
-
-    class gpt_35_turbo:
-        name: str = 'gpt-3.5-turbo'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Wewordle
-
-    class gpt_35_turbo_0613:
-        name: str = 'gpt-3.5-turbo-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Zeabur
-
-    class gpt_35_turbo_0301:
-        name: str = 'gpt-3.5-turbo-0301'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Zeabur
-
-    class gpt_35_turbo_16k_0613:
-        name: str = 'gpt-3.5-turbo-16k-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Zeabur
-
-    class gpt_35_turbo_16k:
-        name: str = 'gpt-3.5-turbo-16k'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.ChatFree
-
-    class gpt_4_dev:
-        name: str = 'gpt-4-for-dev'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Phind
-
-    class gpt_4:
-        name: str = 'gpt-4'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.ChatgptAi
-
-    class gpt_4_0613:
-        name: str = 'gpt-4-0613'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Lockchat
-        best_providers: list = [Provider.Bing, Provider.Lockchat]
-
-    class claude_instant_v1_100k:
-        name: str = 'claude-instant-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_instant_v1:
-        name: str = 'claude-instant-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1_100k:
-        name: str = 'claude-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1:
-        name: str = 'claude-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class alpaca_7b:
-        name: str = 'alpaca-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class stablelm_tuned_alpha_7b:
-        name: str = 'stablelm-tuned-alpha-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloom:
-        name: str = 'bloom'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloomz:
-        name: str = 'bloomz'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_t5_xxl:
-        name: str = 'flan-t5-xxl'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_ul2:
-        name: str = 'flan-ul2'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class gpt_neox_20b:
-        name: str = 'gpt-neox-20b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class oasst_sft_4_pythia_12b_epoch_35:
-        name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class santacoder:
-        name: str = 'santacoder'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_medium_nightly:
-        name: str = 'command-medium-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_xlarge_nightly:
-        name: str = 'command-xlarge-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_cushman_001:
-        name: str = 'code-cushman-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_davinci_002:
-        name: str = 'code-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_ada_001:
-        name: str = 'text-ada-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_babbage_001:
-        name: str = 'text-babbage-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_curie_001:
-        name: str = 'text-curie-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_002:
-        name: str = 'text-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_003:
-        name: str = 'text-davinci-003'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class palm:
-        name: str = 'palm2'
-        base_provider: str = 'google'
-        best_provider: Provider.Provider = Provider.Bard
-
-    class falcon_40b:
-        name: str = 'falcon-40b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-
-    class falcon_7b:
-        name: str = 'falcon-7b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-
-    class llama_13b:
-        name: str = 'llama-13b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-
-
-class ModelUtils:
-    convert: dict = {
-        'gpt-3.5-turbo': Model.gpt_35_turbo,
-        'gpt-3.5-turbo-0613': Model.gpt_35_turbo_0613,
-        'gpt-3.5-turbo-0301': Model.gpt_35_turbo_0301,
-        'gpt-4': Model.gpt_4,
-        'gpt-4-0613': Model.gpt_4_0613,
-        'gpt-4-for-dev': Model.gpt_4_dev,
-        'gpt-3.5-turbo-16k': Model.gpt_35_turbo_16k,
-        'gpt-3.5-turbo-16k-0613': Model.gpt_35_turbo_16k_0613,
-
-        'claude-instant-v1-100k': Model.claude_instant_v1_100k,
-        'claude-v1-100k': Model.claude_v1_100k,
-        'claude-instant-v1': Model.claude_instant_v1,
-        'claude-v1': Model.claude_v1,
-
-        'alpaca-7b': Model.alpaca_7b,
-        'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
-
-        'bloom': Model.bloom,
-        'bloomz': Model.bloomz,
-
-        'flan-t5-xxl': Model.flan_t5_xxl,
-        'flan-ul2': Model.flan_ul2,
-
-        'gpt-neox-20b': Model.gpt_neox_20b,
-        'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
-        'santacoder': Model.santacoder,
-
-        'command-medium-nightly': Model.command_medium_nightly,
-        'command-xlarge-nightly': Model.command_xlarge_nightly,
-
-        'code-cushman-001': Model.code_cushman_001,
-        'code-davinci-002': Model.code_davinci_002,
-
-        'text-ada-001': Model.text_ada_001,
-        'text-babbage-001': Model.text_babbage_001,
-        'text-curie-001': Model.text_curie_001,
-        'text-davinci-002': Model.text_davinci_002,
-        'text-davinci-003': Model.text_davinci_003,
-
-        'palm2': Model.palm,
-        'palm': Model.palm,
-        'google': Model.palm,
-        'google-bard': Model.palm,
-        'google-palm': Model.palm,
-        'bard': Model.palm,
-
-        'falcon-40b': Model.falcon_40b,
-        'falcon-7b': Model.falcon_7b,
-        'llama-13b': Model.llama_13b,
-    }

spaces/CyberHarem/find_my_waifu/civitai.py

@@ -1,26 +0,0 @@
-from gchar.games.dispatch.access import GAME_CHARS
-
-
-def try_find_title(char_name, game_name):
-    try:
-        game_cls = GAME_CHARS[game_name.lower()]
-        ch = game_cls.get(char_name)
-        if ch:
-            names = []
-            if ch.enname:
-                names.append(str(ch.enname))
-            if ch.jpname:
-                names.append(str(ch.jpname))
-            if ch.cnname:
-                names.append(str(ch.cnname))
-            if hasattr(ch, 'krname') and ch.krname:
-                names.append(str(ch.krname))
-
-            return f"{'/'.join(names)} ({game_cls.__official_name__})"
-
-        else:
-            cname = ' '.join(list(map(str.capitalize, char_name.split(' '))))
-            return f'{cname} ({game_cls.__official_name__})'
-
-    except KeyError:
-        return None