parti pytorch
0.2.0
在 Pytorch 中實現 Parti，這是 Google 純基於注意力的文本到圖像神經網路。專案頁面
該儲存庫還包含 ViT VQGan VAE 的工作訓練程式碼。它還包含一些額外的修改，以便根據視覺轉換器文獻進行更快的訓練。
雅尼克·基爾徹
如果您有興趣幫助 LAION 社區進行複製，請加入
$ pip install parti-pytorch首先，您需要訓練您的 Transformer VQ-GAN VAE
from parti_pytorch import VitVQGanVAE , VQGanVAETrainer
vit_vae = VitVQGanVAE (
dim = 256 , # dimensions
image_size = 256 , # target image size
patch_size = 16 , # size of the patches in the image attending to each other
num_layers = 3 # number of layers
). cuda ()
trainer = VQGanVAETrainer (
vit_vae ,
folder = '/path/to/your/images' ,
num_train_steps = 100000 ,
lr = 3e-4 ,
batch_size = 4 ,
grad_accum_every = 8 ,
amp = True
)
trainer . train ()然後
import torch
from parti_pytorch import Parti , VitVQGanVAE
# first instantiate your ViT VQGan VAE
# a VQGan VAE made of transformers
vit_vae = VitVQGanVAE (
dim = 256 , # dimensions
image_size = 256 , # target image size
patch_size = 16 , # size of the patches in the image attending to each other
num_layers = 3 # number of layers
). cuda ()
vit_vae . load_state_dict ( torch . load ( f'/path/to/vae.pt' )) # you will want to load the exponentially moving averaged VAE
# then you plugin the ViT VqGan VAE into your Parti as so
parti = Parti (
vae = vit_vae , # vit vqgan vae
dim = 512 , # model dimension
depth = 8 , # depth
dim_head = 64 , # attention head dimension
heads = 8 , # attention heads
dropout = 0. , # dropout
cond_drop_prob = 0.25 , # conditional dropout, for classifier free guidance
ff_mult = 4 , # feedforward expansion factor
t5_name = 't5-large' , # name of your T5
)
# ready your training text and images
texts = [
'a child screaming at finding a worm within a half-eaten apple' ,
'lizard running across the desert on two feet' ,
'waking up to a psychedelic landscape' ,
'seashells sparkling in the shallow waters'
]
images = torch . randn ( 4 , 3 , 256 , 256 ). cuda ()
# feed it into your parti instance, with return_loss set to True
loss = parti (
texts = texts ,
images = images ,
return_loss = True
)
loss . backward ()
# do this for a long time on much data
# then...
images = parti . generate ( texts = [
'a whale breaching from afar' ,
'young girl blowing out candles on her birthday cake' ,
'fireworks with blue and green sparkles'
], cond_scale = 3. , return_pil_images = True ) # conditioning scale for classifier free guidance
# List[PILImages] (256 x 256 RGB)實際上，在擴展時，您需要將文字預先編碼為標記及其各自的掩碼
from parti_pytorch . t5 import t5_encode_text
images = torch . randn ( 4 , 3 , 256 , 256 ). cuda ()
text_token_embeds , text_mask = t5_encode_text ([
'a child screaming at finding a worm within a half-eaten apple' ,
'lizard running across the desert on two feet' ,
'waking up to a psychedelic landscape' ,
'seashells sparkling in the shallow waters'
], name = 't5-large' , output_device = images . device )
# store somewhere, then load with the dataloader
loss = parti (
text_token_embeds = text_token_embeds ,
text_mask = text_mask ,
images = images ,
return_loss = True
)
loss . backward ()StabilityAI 以及我的其他贊助商為我提供了開源人工智慧的獨立性。
？ Huggingface 的 Transformer 庫以及使用 T5 語言模型輕鬆編碼文本
@inproceedings { Yu2022Pathways
title = { Pathways Autoregressive Text-to-Image Model } ,
author = { Jiahui Yu*, Yuanzhong Xu†, Jing Yu Koh†, Thang Luong†, Gunjan Baid†, Zirui Wang†, Vijay Vasudevan†, Alexander Ku†, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge†, Yonghui Wu* } ,
year = { 2022 }
} @article { Shleifer2021NormFormerIT ,
title = { NormFormer: Improved Transformer Pretraining with Extra Normalization } ,
author = { Sam Shleifer and Jason Weston and Myle Ott } ,
journal = { ArXiv } ,
year = { 2021 } ,
volume = { abs/2110.09456 }
} @article { Sankararaman2022BayesFormerTW ,
title = { BayesFormer: Transformer with Uncertainty Estimation } ,
author = { Karthik Abinav Sankararaman and Sinong Wang and Han Fang } ,
journal = { ArXiv } ,
year = { 2022 } ,
volume = { abs/2206.00826 }
} @article { Lee2021VisionTF ,
title = { Vision Transformer for Small-Size Datasets } ,
author = { Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song } ,
journal = { ArXiv } ,
year = { 2021 } ,
volume = { abs/2112.13492 }
} @article { Chu2021DoWR ,
title = { Do We Really Need Explicit Position Encodings for Vision Transformers? } ,
author = { Xiangxiang Chu and Bo Zhang and Zhi Tian and Xiaolin Wei and Huaxia Xia } ,
journal = { ArXiv } ,
year = { 2021 } ,
volume = { abs/2102.10882 }
} @article { So2021PrimerSF ,
title = { Primer: Searching for Efficient Transformers for Language Modeling } ,
author = { David R. So and Wojciech Ma'nke and Hanxiao Liu and Zihang Dai and Noam M. Shazeer and Quoc V. Le } ,
journal = { ArXiv } ,
year = { 2021 } ,
volume = { abs/2109.08668 }
} @inproceedings { Wang2021CrossFormerAV ,
title = { CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention } ,
author = { Wenxiao Wang and Lulian Yao and Long Chen and Binbin Lin and Deng Cai and Xiaofei He and Wei Liu } ,
year = { 2021 }
} @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 }
}
