mmdit

MMDiT

Implementasi MMDiT satu lapis, diusulkan oleh Esser et al. di Difusi Stabil 3, di Pytorch

Selain reproduksi lurus, saya juga akan menggeneralisasi menjadi> 2 modalitas, karena saya dapat membayangkan MMDiT untuk gambar, audio, dan teks.

Juga akan menawarkan varian perhatian diri improvisasi yang secara adaptif memilih bobot yang akan digunakan melalui pembelajaran gating. Ide ini berasal dari konvolusi adaptif yang diterapkan oleh Kang et al. untuk GigaGAN.

Memasang

$ pip install mmdit

Penggunaan

 import torch
from mmdit import MMDiTBlock

# define mm dit block

block = MMDiTBlock (
    dim_joint_attn = 512 ,
    dim_cond = 256 ,
    dim_text = 768 ,
    dim_image = 512 ,
    qk_rmsnorm = True
)

# mock inputs

time_cond = torch . randn ( 2 , 256 )

text_tokens = torch . randn ( 2 , 512 , 768 )
text_mask = torch . ones (( 2 , 512 )). bool ()

image_tokens = torch . randn ( 2 , 1024 , 512 )

# single block forward

text_tokens_next , image_tokens_next = block (
    time_cond = time_cond ,
    text_tokens = text_tokens ,
    text_mask = text_mask ,
    image_tokens = image_tokens
)

Versi umum dapat digunakan demikian

 import torch
from mmdit . mmdit_generalized_pytorch import MMDiT

mmdit = MMDiT (
    depth = 2 , 
    dim_modalities = ( 768 , 512 , 384 ),
    dim_joint_attn = 512 ,
    dim_cond = 256 ,
    qk_rmsnorm = True
)

# mock inputs

time_cond = torch . randn ( 2 , 256 )

text_tokens = torch . randn ( 2 , 512 , 768 )
text_mask = torch . ones (( 2 , 512 )). bool ()

video_tokens = torch . randn ( 2 , 1024 , 512 )

audio_tokens = torch . randn ( 2 , 256 , 384 )

# forward

text_tokens , video_tokens , audio_tokens = mmdit (
    modality_tokens = ( text_tokens , video_tokens , audio_tokens ),
    modality_masks = ( text_mask , None , None ),
    time_cond = time_cond ,
)

Kutipan

 @article { Esser2024ScalingRF ,
    title   = { Scaling Rectified Flow Transformers for High-Resolution Image Synthesis } ,
    author  = { Patrick Esser and Sumith Kulal and A. Blattmann and Rahim Entezari and Jonas Muller and Harry Saini and Yam Levi and Dominik Lorenz and Axel Sauer and Frederic Boesel and Dustin Podell and Tim Dockhorn and Zion English and Kyle Lacey and Alex Goodwin and Yannik Marek and Robin Rombach } ,
    journal = { ArXiv } ,
    year    = { 2024 } ,
    volume  = { abs/2403.03206 } ,
    url     = { https://api.semanticscholar.org/CorpusID:268247980 }
}

 @inproceedings { Darcet2023VisionTN ,
    title   = { Vision Transformers Need Registers } ,
    author  = { Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski } ,
    year    = { 2023 } ,
    url     = { https://api.semanticscholar.org/CorpusID:263134283 }
}

 @article { Zhu2024HyperConnections ,
    title   = { Hyper-Connections } ,
    author  = { Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou } ,
    journal = { ArXiv } ,
    year    = { 2024 } ,
    volume  = { abs/2409.19606 } ,
    url     = { https://api.semanticscholar.org/CorpusID:272987528 }
}