nuwa pytorch
0.7.8
Implementação de NÜWA, rede de atenção de última geração para síntese de texto para vídeo, em Pytorch. Ele também contém uma extensão para geração de vídeo e áudio, usando uma abordagem de decodificador duplo.
Yannic Kilcher
Leitor profundo
Março de 2022 – vendo sinais de vida com uma versão difícil de mover mnist
Abril de 2022 – Parece que um método baseado em difusão assumiu o novo trono para SOTA. No entanto, continuarei com NUWA, estendendo-o para usar códigos multicabeças + transformador causal hierárquico. Acho que essa direção está inexplorada para melhorar essa linha de trabalho.
$ pip install nuwa-pytorchPrimeiro treine o VAE
import torch
from nuwa_pytorch import VQGanVAE
vae = VQGanVAE (
dim = 512 ,
channels = 3 , # default is 3, but can be changed to any value for the training of the segmentation masks (sketches)
image_size = 256 , # image size
num_layers = 4 , # number of downsampling layers
num_resnet_blocks = 2 , # number of resnet blocks
vq_codebook_size = 8192 , # codebook size
vq_decay = 0.8 # codebook exponential decay
)
imgs = torch . randn ( 10 , 3 , 256 , 256 )
# alternate learning for autoencoder ...
loss = vae ( imgs , return_loss = True )
loss . backward ()
# and the discriminator ...
discr_loss = vae ( imgs , return_discr_loss = True )
discr_loss . backward ()
# do above for many steps
# return reconstructed images and make sure they look ok
recon_imgs = vae ( imgs )Então, com seu VAE aprendido
import torch
from nuwa_pytorch import NUWA , VQGanVAE
# autoencoder
vae = VQGanVAE (
dim = 64 ,
num_layers = 4 ,
image_size = 256 ,
num_conv_blocks = 2 ,
vq_codebook_size = 8192
)
# NUWA transformer
nuwa = NUWA (
vae = vae ,
dim = 512 ,
text_num_tokens = 20000 , # number of text tokens
text_enc_depth = 12 , # text encoder depth
text_enc_heads = 8 , # number of attention heads for encoder
text_max_seq_len = 256 , # max sequence length of text conditioning tokens (keep at 256 as in paper, or shorter, if your text is not that long)
max_video_frames = 10 , # number of video frames
image_size = 256 , # size of each frame of video
dec_depth = 64 , # video decoder depth
dec_heads = 8 , # number of attention heads in decoder
dec_reversible = True , # reversible networks - from reformer, decoupling memory usage from depth
enc_reversible = True , # reversible encoders, if you need it
attn_dropout = 0.05 , # dropout for attention
ff_dropout = 0.05 , # dropout for feedforward
sparse_3dna_kernel_size = ( 5 , 3 , 3 ), # kernel size of the sparse 3dna attention. can be a single value for frame, height, width, or different values (to simulate axial attention, etc)
sparse_3dna_dilation = ( 1 , 2 , 4 ), # cycle dilation of 3d conv attention in decoder, for more range
shift_video_tokens = True # cheap relative positions for sparse 3dna transformer, by shifting along spatial dimensions by one
). cuda ()
# data
text = torch . randint ( 0 , 20000 , ( 1 , 256 )). cuda ()
video = torch . randn ( 1 , 10 , 3 , 256 , 256 ). cuda () # (batch, frames, channels, height, width)
loss = nuwa (
text = text ,
video = video ,
return_loss = True # set this to True, only for training, to return cross entropy loss
)
loss . backward ()
# do above with as much data as possible
# then you can generate a video from text
video = nuwa . generate ( text = text , num_frames = 5 ) # (1, 5, 3, 256, 256) No artigo, eles também apresentam uma forma de condicionar a geração de vídeo com base em máscara(s) de segmentação. Você também pode fazer isso facilmente, desde que treine um VQGanVAE nos esboços com antecedência.
Então, você usará NUWASketch em vez de NUWA , que pode aceitar o esboço VAE como referência
ex.
import torch
from nuwa_pytorch import NUWASketch , VQGanVAE
# autoencoder, one for main video, the other for the sketch
vae = VQGanVAE (
dim = 64 ,
num_layers = 4 ,
image_size = 256 ,
num_conv_blocks = 2 ,
vq_codebook_size = 8192
)
sketch_vae = VQGanVAE (
dim = 512 ,
channels = 5 , # say the sketch has 5 classes
num_layers = 4 ,
image_size = 256 ,
num_conv_blocks = 2 ,
vq_codebook_size = 8192
)
# NUWA transformer for conditioning with sketches
nuwa = NUWASketch (
vae = vae ,
sketch_vae = sketch_vae ,
dim = 512 , # model dimensions
sketch_enc_depth = 12 , # sketch encoder depth
sketch_enc_heads = 8 , # number of attention heads for sketch encoder
sketch_max_video_frames = 3 , # max number of frames for sketches
sketch_enc_use_sparse_3dna = True , # whether to use 3d-nearby attention (of full attention if False) for sketch encoding transformer
max_video_frames = 10 , # number of video frames
image_size = 256 , # size of each frame of video
dec_depth = 64 , # video decoder depth
dec_heads = 8 , # number of attention heads in decoder
dec_reversible = True , # reversible networks - from reformer, decoupling memory usage from depth
enc_reversible = True , # reversible encoders, if you need it
attn_dropout = 0.05 , # dropout for attention
ff_dropout = 0.05 , # dropout for feedforward
sparse_3dna_kernel_size = ( 5 , 3 , 3 ), # kernel size of the sparse 3dna attention. can be a single value for frame, height, width, or different values (to simulate axial attention, etc)
sparse_3dna_dilation = ( 1 , 2 , 4 ), # cycle dilation of 3d conv attention in decoder, for more range
cross_2dna_kernel_size = 5 , # 2d kernel size of spatial grouping of attention from video frames to sketches
cross_2dna_dilation = 1 , # 2d dilation of spatial attention from video frames to sketches
shift_video_tokens = True # cheap relative positions for sparse 3dna transformer, by shifting along spatial dimensions by one
). cuda ()
# data
sketch = torch . randn ( 2 , 2 , 5 , 256 , 256 ). cuda () # (batch, frames, segmentation classes, height, width)
sketch_mask = torch . ones ( 2 , 2 ). bool (). cuda () # (batch, frames) [Optional]
video = torch . randn ( 2 , 10 , 3 , 256 , 256 ). cuda () # (batch, frames, channels, height, width)
loss = nuwa (
sketch = sketch ,
sketch_mask = sketch_mask ,
video = video ,
return_loss = True # set this to True, only for training, to return cross entropy loss
)
loss . backward ()
# do above with as much data as possible
# then you can generate a video from sketch(es)
video = nuwa . generate ( sketch = sketch , num_frames = 5 ) # (1, 5, 3, 256, 256) Este repositório também oferecerá uma variante do NUWA que pode produzir vídeo e áudio. Por enquanto, o áudio precisará ser codificado manualmente.
import torch
from nuwa_pytorch import NUWAVideoAudio , VQGanVAE
# autoencoder
vae = VQGanVAE (
dim = 64 ,
num_layers = 4 ,
image_size = 256 ,
num_conv_blocks = 2 ,
vq_codebook_size = 100
)
# NUWA transformer
nuwa = NUWAVideoAudio (
vae = vae ,
dim = 512 ,
num_audio_tokens = 2048 , # codebook size for audio tokens
num_audio_tokens_per_video_frame = 32 , # number of audio tokens per video frame
cross_modality_attn_every = 3 , # cross modality attention every N layers
text_num_tokens = 20000 , # number of text tokens
text_enc_depth = 1 , # text encoder depth
text_enc_heads = 8 , # number of attention heads for encoder
text_max_seq_len = 256 , # max sequence length of text conditioning tokens (keep at 256 as in paper, or shorter, if your text is not that long)
max_video_frames = 10 , # number of video frames
image_size = 256 , # size of each frame of video
dec_depth = 4 , # video decoder depth
dec_heads = 8 , # number of attention heads in decoder
enc_reversible = True , # reversible encoders, if you need it
dec_reversible = True , # quad-branched reversible network, for making depth of twin video / audio decoder independent of network depth. recommended to be turned on unless you have a ton of memory at your disposal
attn_dropout = 0.05 , # dropout for attention
ff_dropout = 0.05 , # dropout for feedforward
sparse_3dna_kernel_size = ( 5 , 3 , 3 ), # kernel size of the sparse 3dna attention. can be a single value for frame, height, width, or different values (to simulate axial attention, etc)
sparse_3dna_dilation = ( 1 , 2 , 4 ), # cycle dilation of 3d conv attention in decoder, for more range
shift_video_tokens = True # cheap relative positions for sparse 3dna transformer, by shifting along spatial dimensions by one
). cuda ()
# data
text = torch . randint ( 0 , 20000 , ( 1 , 256 )). cuda ()
audio = torch . randint ( 0 , 2048 , ( 1 , 32 * 10 )). cuda () # (batch, audio tokens per frame * max video frames)
video = torch . randn ( 1 , 10 , 3 , 256 , 256 ). cuda () # (batch, frames, channels, height, width)
loss = nuwa (
text = text ,
video = video ,
audio = audio ,
return_loss = True # set this to True, only for training, to return cross entropy loss
)
loss . backward ()
# do above with as much data as possible
# then you can generate a video from text
video , audio = nuwa . generate ( text = text , num_frames = 5 ) # (1, 5, 3, 256, 256), (1, 32 * 5 == 160) Esta biblioteca oferecerá alguns utilitários para facilitar o treinamento. Para começar, você pode usar a classe VQGanVAETrainer para cuidar do treinamento do VQGanVAE . Basta agrupar o modelo e passar também o caminho da pasta de imagens, bem como os diversos hiperparâmetros de treinamento.
import torch
from nuwa_pytorch import VQGanVAE , VQGanVAETrainer
vae = VQGanVAE (
dim = 64 ,
image_size = 256 ,
num_layers = 5 ,
vq_codebook_size = 1024 ,
vq_use_cosine_sim = True ,
vq_codebook_dim = 32 ,
vq_orthogonal_reg_weight = 10 ,
vq_orthogonal_reg_max_codes = 128 ,
). cuda ()
trainer = VQGanVAETrainer (
vae , # VAE defined above
folder = '/path/to/images' , # path to images
lr = 3e-4 , # learning rate
num_train_steps = 100000 , # number of training steps
batch_size = 8 , # batch size
grad_accum_every = 4 # gradient accumulation (effective batch size is (batch_size x grad_accum_every))
)
trainer . train ()
# results and model checkpoints will be saved periodically to ./results Para treinar o NUWA, primeiro você precisa organizar uma pasta de arquivos .gif com os arquivos .txt correspondentes contendo sua legenda. Deveria ser organizado como tal.
ex.
video-and-text-data
┣ cat.gif
┣ cat.txt
┣ dog.gif
┣ dog.txt
┣ turtle.gif
┗ turtle.txt
Em seguida, você carregará seu VQGan-VAE previamente treinado e treinará NUWA com as classes GifVideoDataset e NUWATrainer .
import torch
from nuwa_pytorch import NUWA , VQGanVAE
from nuwa_pytorch . train_nuwa import GifVideoDataset , NUWATrainer
# dataset
ds = GifVideoDataset (
folder = './path/to/videos/' ,
channels = 1
)
# autoencoder
vae = VQGanVAE (
dim = 64 ,
image_size = 256 ,
num_layers = 5 ,
num_resnet_blocks = 2 ,
vq_codebook_size = 512 ,
attn_dropout = 0.1
)
vae . load_state_dict ( torch . load ( './path/to/trained/vae.pt' ))
# NUWA transformer
nuwa = NUWA (
vae = vae ,
dim = 512 ,
text_enc_depth = 6 ,
text_max_seq_len = 256 ,
max_video_frames = 10 ,
dec_depth = 12 ,
dec_reversible = True ,
enc_reversible = True ,
attn_dropout = 0.05 ,
ff_dropout = 0.05 ,
sparse_3dna_kernel_size = ( 5 , 3 , 3 ),
sparse_3dna_dilation = ( 1 , 2 , 4 ),
shift_video_tokens = True
). cuda ()
# data
trainer = NUWATrainer (
nuwa = nuwa , # NUWA transformer
dataset = dataset , # video dataset class
num_train_steps = 1000000 , # number of training steps
lr = 3e-4 , # learning rate
wd = 0.01 , # weight decay
batch_size = 8 , # batch size
grad_accum_every = 4 , # gradient accumulation
max_grad_norm = 0.5 , # gradient clipping
num_sampled_frames = 10 , # number of frames to sample
results_folder = './results' # folder to store checkpoints and samples
)
trainer . train () Esta biblioteca depende desta biblioteca de quantização vetorial, que vem com uma série de melhorias (vqgan aprimorado, regularização ortogonal do livro de códigos, etc). Para usar qualquer uma dessas melhorias, você pode configurar os parâmetros de palavra-chave do quantizador vetorial acrescentando vq_ na inicialização VQGanVAE .
ex. cosseno sim proposto em vqgan melhorado
from nuwa_pytorch import VQGanVAE
vae = VQGanVAE (
dim = 64 ,
image_size = 256 ,
num_layers = 4 ,
vq_use_cosine_sim = True
# VectorQuantize will be initialized with use_cosine_sim = True
# https://github.com/lucidrains/vector-quantize-pytorch#cosine-similarity
). cuda () @misc { wu2021nuwa ,
title = { N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion } ,
author = { Chenfei Wu and Jian Liang and Lei Ji and Fan Yang and Yuejian Fang and Daxin Jiang and Nan Duan } ,
year = { 2021 } ,
eprint = { 2111.12417 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CV }
} @misc { esser2021taming ,
title = { Taming Transformers for High-Resolution Image Synthesis } ,
author = { Patrick Esser and Robin Rombach and Björn Ommer } ,
year = { 2021 } ,
eprint = { 2012.09841 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CV }
} @misc { iashin2021taming ,
title = { Taming Visually Guided Sound Generation } ,
author = { Vladimir Iashin and Esa Rahtu } ,
year = { 2021 } ,
eprint = { 2110.08791 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CV }
} @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 { kitaev2020reformer ,
title = { Reformer: The Efficient Transformer } ,
author = { Nikita Kitaev and Łukasz Kaiser and Anselm Levskaya } ,
year = { 2020 } ,
eprint = { 2001.04451 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.LG }
} @misc { shazeer2020talkingheads ,
title = { Talking-Heads Attention } ,
author = { Noam Shazeer and Zhenzhong Lan and Youlong Cheng and Nan Ding and Le Hou } ,
year = { 2020 } ,
eprint = { 2003.02436 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.LG }
} @misc { shazeer2020glu ,
title = { GLU Variants Improve Transformer } ,
author = { Noam Shazeer } ,
year = { 2020 } ,
url = { https://arxiv.org/abs/2002.05202 }
} @misc { su2021roformer ,
title = { RoFormer: Enhanced Transformer with Rotary Position Embedding } ,
author = { Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu } ,
year = { 2021 } ,
eprint = { 2104.09864 } ,
archivePrefix = { arXiv } ,
primaryClass = { cs.CL }
} @inproceedings { ho2021classifierfree ,
title = { Classifier-Free Diffusion Guidance } ,
author = { Jonathan Ho and Tim Salimans } ,
booktitle = { NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications } ,
year = { 2021 } ,
url = { https://openreview.net/forum?id=qw8AKxfYbI }
} @misc { liu2021swin ,
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 } ,
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archivePrefix = { arXiv } ,
primaryClass = { cs.CV }
} @misc { crowson2022 ,
author = { Katherine Crowson } ,
url = { https://twitter.com/RiversHaveWings/status/1478093658716966912 }
}A atenção é a forma mais rara e pura de generosidade. -Simone Weil
