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Фенаки - Питорх

Реализация Phenaki Video, которая использует Mask GIT для создания видео с текстовыми инструкциями продолжительностью до 2 минут в Pytorch. Он также будет сочетать в себе еще один метод, включающий символическую критику потенциально еще лучших поколений.

Пожалуйста, присоединяйтесь, если вы заинтересованы в воспроизведении этой работы в открытом доступе.

Объяснение кофе-брейка AI

Признательность

Stability.ai за щедрую спонсорскую поддержку передовых исследований в области искусственного интеллекта.
? Huggingface за их потрясающие трансформеры и библиотеку ускорения.
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Ты? Если вы отличный инженер и/или исследователь машинного обучения, не стесняйтесь внести свой вклад в развитие генеративного искусственного интеллекта с открытым исходным кодом.

Установить

$ pip install phenaki-pytorch

Использование

C-ВиВиТ

 import torch
from phenaki_pytorch import CViViT , CViViTTrainer

cvivit = CViViT (
    dim = 512 ,
    codebook_size = 65536 ,
    image_size = 256 ,
    patch_size = 32 ,
    temporal_patch_size = 2 ,
    spatial_depth = 4 ,
    temporal_depth = 4 ,
    dim_head = 64 ,
    heads = 8
). cuda ()

trainer = CViViTTrainer (
    cvivit ,
    folder = '/path/to/images/or/videos' ,
    batch_size = 4 ,
    grad_accum_every = 4 ,
    train_on_images = False ,  # you can train on images first, before fine tuning on video, for sample efficiency
    use_ema = False ,          # recommended to be turned on (keeps exponential moving averaged cvivit) unless if you don't have enough resources
    num_train_steps = 10000
)

trainer . train ()               # reconstructions and checkpoints will be saved periodically to ./results

Фенаки

 import torch
from phenaki_pytorch import CViViT , MaskGit , Phenaki

cvivit = CViViT (
    dim = 512 ,
    codebook_size = 65536 ,
    image_size = ( 256 , 128 ),  # video with rectangular screen allowed
    patch_size = 32 ,
    temporal_patch_size = 2 ,
    spatial_depth = 4 ,
    temporal_depth = 4 ,
    dim_head = 64 ,
    heads = 8
)

cvivit . load ( '/path/to/trained/cvivit.pt' )

maskgit = MaskGit (
    num_tokens = 5000 ,
    max_seq_len = 1024 ,
    dim = 512 ,
    dim_context = 768 ,
    depth = 6 ,
)

phenaki = Phenaki (
    cvivit = cvivit ,
    maskgit = maskgit
). cuda ()

videos = torch . randn ( 3 , 3 , 17 , 256 , 128 ). cuda () # (batch, channels, frames, height, width)
mask = torch . ones (( 3 , 17 )). bool (). cuda () # [optional] (batch, frames) - allows for co-training videos of different lengths as well as video and images in the same batch

texts = [
    'a whale breaching from afar' ,
    'young girl blowing out candles on her birthday cake' ,
    'fireworks with blue and green sparkles'
]

loss = phenaki ( videos , texts = texts , video_frame_mask = mask )
loss . backward ()

# do the above for many steps, then ...

video = phenaki . sample ( texts = 'a squirrel examines an acorn' , num_frames = 17 , cond_scale = 5. ) # (1, 3, 17, 256, 128)

# so in the paper, they do not really achieve 2 minutes of coherent video
# at each new scene with new text conditioning, they condition on the previous K frames
# you can easily achieve this with this framework as so

video_prime = video [:, :, - 3 :] # (1, 3, 3, 256, 128) # say K = 3

video_next = phenaki . sample ( texts = 'a cat watches the squirrel from afar' , prime_frames = video_prime , num_frames = 14 ) # (1, 3, 14, 256, 128)

# the total video

entire_video = torch . cat (( video , video_next ), dim = 2 ) # (1, 3, 17 + 14, 256, 128)

# and so on...

Или просто импортируйте функцию make_video

 # ... above code

from phenaki_pytorch import make_video

entire_video , scenes = make_video ( phenaki , texts = [
    'a squirrel examines an acorn buried in the snow' ,
    'a cat watches the squirrel from a frosted window sill' ,
    'zoom out to show the entire living room, with the cat residing by the window sill'
], num_frames = ( 17 , 14 , 14 ), prime_lengths = ( 5 , 5 ))

entire_video . shape # (1, 3, 17 + 14 + 14 = 45, 256, 256)

# scenes - List[Tensor[3]] - video segment of each scene

Вот и все!

Токен Критик

В новой статье предполагается, что вместо того, чтобы полагаться на предсказанные вероятности каждого токена как на меру уверенности, можно обучить дополнительного критика решать, что итеративно маскировать во время выборки. При желании вы можете обучить этого критика потенциально лучшим поколениям, как показано ниже.

 import torch
from phenaki_pytorch import CViViT , MaskGit , TokenCritic , Phenaki

cvivit = CViViT (
    dim = 512 ,
    codebook_size = 65536 ,
    image_size = ( 256 , 128 ),
    patch_size = 32 ,
    temporal_patch_size = 2 ,
    spatial_depth = 4 ,
    temporal_depth = 4 ,
    dim_head = 64 ,
    heads = 8
)

maskgit = MaskGit (
    num_tokens = 65536 ,
    max_seq_len = 1024 ,
    dim = 512 ,
    dim_context = 768 ,
    depth = 6 ,
)

# (1) define the critic

critic = TokenCritic (
    num_tokens = 65536 ,
    max_seq_len = 1024 ,
    dim = 512 ,
    dim_context = 768 ,
    depth = 6 ,
    has_cross_attn = True
)

trainer = Phenaki (
    maskgit = maskgit ,
    cvivit = cvivit ,
    critic = critic    # and then (2) pass it into Phenaki
). cuda ()

texts = [
    'a whale breaching from afar' ,
    'young girl blowing out candles on her birthday cake' ,
    'fireworks with blue and green sparkles'
]

videos = torch . randn ( 3 , 3 , 3 , 256 , 128 ). cuda () # (batch, channels, frames, height, width)

loss = trainer ( videos = videos , texts = texts )
loss . backward ()

Или, что еще проще, просто повторно используйте сам MaskGit в качестве самокритика (Нийкамп и др.), установив self_token_critic = True при инициализации Phenaki

 phenaki = Phenaki (
    ...,
    self_token_critic = True  # set this to True
)

Теперь ваши поколения должны значительно улучшиться!

Тренер Фенаки

Этот репозиторий также будет направлен на то, чтобы позволить исследователям обучаться преобразованию текста в изображение, а затем преобразования текста в видео. Аналогично, для безусловного обучения исследователь должен иметь возможность сначала тренироваться на изображениях, а затем точно настраивать видео. Ниже приведен пример преобразования текста в видео.

 import torch
from torch . utils . data import Dataset
from phenaki_pytorch import CViViT , MaskGit , Phenaki , PhenakiTrainer

cvivit = CViViT (
    dim = 512 ,
    codebook_size = 65536 ,
    image_size = 256 ,
    patch_size = 32 ,
    temporal_patch_size = 2 ,
    spatial_depth = 4 ,
    temporal_depth = 4 ,
    dim_head = 64 ,
    heads = 8
)

cvivit . load ( '/path/to/trained/cvivit.pt' )

maskgit = MaskGit (
    num_tokens = 5000 ,
    max_seq_len = 1024 ,
    dim = 512 ,
    dim_context = 768 ,
    depth = 6 ,
    unconditional = False
)

phenaki = Phenaki (
    cvivit = cvivit ,
    maskgit = maskgit
). cuda ()

# mock text video dataset
# you will have to extend your own, and return the (<video tensor>, <caption>) tuple

class MockTextVideoDataset ( Dataset ):
    def __init__ (
        self ,
        length = 100 ,
        image_size = 256 ,
        num_frames = 17
    ):
        super (). __init__ ()
        self . num_frames = num_frames
        self . image_size = image_size
        self . len = length

    def __len__ ( self ):
        return self . len

    def __getitem__ ( self , idx ):
        video = torch . randn ( 3 , self . num_frames , self . image_size , self . image_size )
        caption = 'video caption'
        return video , caption

dataset = MockTextVideoDataset ()

# pass in the dataset

trainer = PhenakiTrainer (
    phenaki = phenaki ,
    batch_size = 4 ,
    grad_accum_every = 4 ,
    train_on_images = False , # if your mock dataset above return (images, caption) pairs, set this to True
    dataset = dataset ,       # pass in your dataset here
    sample_texts_file_path = '/path/to/captions.txt' # each caption should be on a new line, during sampling, will be randomly drawn
)

trainer . train ()

Безусловный заключается в следующем

бывший. безусловные изображения и видео обучение

 import torch
from phenaki_pytorch import CViViT , MaskGit , Phenaki , PhenakiTrainer

cvivit = CViViT (
    dim = 512 ,
    codebook_size = 65536 ,
    image_size = 256 ,
    patch_size = 32 ,
    temporal_patch_size = 2 ,
    spatial_depth = 4 ,
    temporal_depth = 4 ,
    dim_head = 64 ,
    heads = 8
)

cvivit . load ( '/path/to/trained/cvivit.pt' )

maskgit = MaskGit (
    num_tokens = 5000 ,
    max_seq_len = 1024 ,
    dim = 512 ,
    dim_context = 768 ,
    depth = 6 ,
    unconditional = False
)

phenaki = Phenaki (
    cvivit = cvivit ,
    maskgit = maskgit
). cuda ()

# pass in the folder to images or video

trainer = PhenakiTrainer (
    phenaki = phenaki ,
    batch_size = 4 ,
    grad_accum_every = 4 ,
    train_on_images = True ,                # for sake of example, bottom is folder of images
    dataset = '/path/to/images/or/video'
)

trainer . train ()

Тодо

Цитаты

 @article { Villegas2022PhenakiVL ,
    title   = { Phenaki: Variable Length Video Generation From Open Domain Textual Description } ,
    author  = { Ruben Villegas and Mohammad Babaeizadeh and Pieter-Jan Kindermans and Hernan Moraldo and Han Zhang and Mohammad Taghi Saffar and Santiago Castro and Julius Kunze and D. Erhan } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2210.02399 }
}

 @article { Chang2022MaskGITMG ,
    title   = { MaskGIT: Masked Generative Image Transformer } ,
    author  = { Huiwen Chang and Han Zhang and Lu Jiang and Ce Liu and William T. Freeman } ,
    journal = { 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) } ,
    year    = { 2022 } ,
    pages   = { 11305-11315 }
}

 @article { Lezama2022ImprovedMI ,
    title   = { Improved Masked Image Generation with Token-Critic } ,
    author  = { Jos{'e} Lezama and Huiwen Chang and Lu Jiang and Irfan Essa } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2209.04439 }
}

 @misc { ding2021cogview ,
    title   = { CogView: Mastering Text-to-Image Generation via Transformers } ,
    author  = { Ming Ding and Zhuoyi Yang and Wenyi Hong and Wendi Zheng and Chang Zhou and Da Yin and Junyang Lin and Xu Zou and Zhou Shao and Hongxia Yang and Jie Tang } ,
    year    = { 2021 } ,
    eprint  = { 2105.13290 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}

 @misc { shazeer2020glu ,
    title   = { GLU Variants Improve Transformer } ,
    author  = { Noam Shazeer } ,
    year    = { 2020 } ,
    url     = { https://arxiv.org/abs/2002.05202 }
}

 @misc { press2021ALiBi ,
    title   = { Train Short, Test Long: Attention with Linear Biases Enable Input Length Extrapolation } ,
    author  = { Ofir Press and Noah A. Smith and Mike Lewis } ,
    year    = { 2021 } ,
    url     = { https://ofir.io/train_short_test_long.pdf }
}

 @article { Liu2022SwinTV ,
    title   = { Swin Transformer V2: Scaling Up Capacity and Resolution } ,
    author  = { Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo } ,
    journal = { 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) } ,
    year    = { 2022 } ,
    pages   = { 11999-12009 }
}

 @inproceedings { Nijkamp2021SCRIPTSP ,
    title   = { SCRIPT: Self-Critic PreTraining of Transformers } ,
    author  = { Erik Nijkamp and Bo Pang and Ying Nian Wu and Caiming Xiong } ,
    booktitle = { North American Chapter of the Association for Computational Linguistics } ,
    year    = { 2021 }
}

 @misc { https://doi.org/10.48550/arxiv.2302.01327 ,
    doi     = { 10.48550/ARXIV.2302.01327 } ,
    url     = { https://arxiv.org/abs/2302.01327 } ,
    author  = { Kumar, Manoj and Dehghani, Mostafa and Houlsby, Neil } ,
    title   = { Dual PatchNorm } ,
    publisher = { arXiv } ,
    year    = { 2023 } ,
    copyright = { Creative Commons Attribution 4.0 International }
}

 @misc { gilmer2023intriguing
    title  = { Intriguing Properties of Transformer Training Instabilities } ,
    author = { Justin Gilmer, Andrea Schioppa, and Jeremy Cohen } ,
    year   = { 2023 } ,
    status = { to be published - one attention stabilization technique is circulating within Google Brain, being used by multiple teams }
}

 @misc { mentzer2023finite ,
    title   = { Finite Scalar Quantization: VQ-VAE Made Simple } ,
    author  = { Fabian Mentzer and David Minnen and Eirikur Agustsson and Michael Tschannen } ,
    year    = { 2023 } ,
    eprint  = { 2309.15505 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}

 @misc { yu2023language ,
    title   = { Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation } ,
    author  = { Lijun Yu and José Lezama and Nitesh B. Gundavarapu and Luca Versari and Kihyuk Sohn and David Minnen and Yong Cheng and Agrim Gupta and Xiuye Gu and Alexander G. Hauptmann and Boqing Gong and Ming-Hsuan Yang and Irfan Essa and David A. Ross and Lu Jiang } ,
    year    = { 2023 } ,
    eprint  = { 2310.05737 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CV }
}