autoregressive diffusion pytorch

자기회귀 확산 - Pytorch

Pytorch에서 벡터 양자화 없이 자동 회귀 이미지 생성 기반 아키텍처 구현

공식 저장소가 여기에 공개되었습니다

대체 경로

96,000걸음의 옥스포드 꽃

설치하다

$ pip install autoregressive-diffusion-pytorch

용법

 import torch
from autoregressive_diffusion_pytorch import AutoregressiveDiffusion

model = AutoregressiveDiffusion (
    dim_input = 512 ,
    dim = 1024 ,
    max_seq_len = 32 ,
    depth = 8 ,
    mlp_depth = 3 ,
    mlp_width = 1024
)

seq = torch . randn ( 3 , 32 , 512 )

loss = model ( seq )
loss . backward ()

sampled = model . sample ( batch_size = 3 )

assert sampled . shape == seq . shape

일련의 토큰으로 처리되는 이미지의 경우(종이와 같이)

 import torch
from autoregressive_diffusion_pytorch import ImageAutoregressiveDiffusion

model = ImageAutoregressiveDiffusion (
    model = dict (
        dim = 1024 ,
        depth = 12 ,
        heads = 12 ,
    ),
    image_size = 64 ,
    patch_size = 8
)

images = torch . randn ( 3 , 3 , 64 , 64 )

loss = model ( images )
loss . backward ()

sampled = model . sample ( batch_size = 3 )

assert sampled . shape == images . shape

이미지 트레이너

 import torch

from autoregressive_diffusion_pytorch import (
    ImageDataset ,
    ImageAutoregressiveDiffusion ,
    ImageTrainer
)

dataset = ImageDataset (
    '/path/to/your/images' ,
    image_size = 128
)

model = ImageAutoregressiveDiffusion (
    model = dict (
        dim = 512
    ),
    image_size = 128 ,
    patch_size = 16
)

trainer = ImageTrainer (
    model = model ,
    dataset = dataset
)

trainer ()

흐름 일치를 사용하는 즉석 버전의 경우 대신 ImageAutoregressiveFlow 및 AutoregressiveFlow 가져오세요.

나머지는 똑같음

전.

 import torch

from autoregressive_diffusion_pytorch import (
    ImageDataset ,
    ImageTrainer ,
    ImageAutoregressiveFlow ,
)

dataset = ImageDataset (
    '/path/to/your/images' ,
    image_size = 128
)

model = ImageAutoregressiveFlow (
    model = dict (
        dim = 512
    ),
    image_size = 128 ,
    patch_size = 16
)

trainer = ImageTrainer (
    model = model ,
    dataset = dataset
)

trainer ()

인용

 @article { Li2024AutoregressiveIG ,
    title   = { Autoregressive Image Generation without Vector Quantization } ,
    author  = { Tianhong Li and Yonglong Tian and He Li and Mingyang Deng and Kaiming He } ,
    journal = { ArXiv } ,
    year    = { 2024 } ,
    volume  = { abs/2406.11838 } ,
    url     = { https://api.semanticscholar.org/CorpusID:270560593 }
}

 @article { Wu2023ARDiffusionAD ,
    title     = { AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation } ,
    author    = { Tong Wu and Zhihao Fan and Xiao Liu and Yeyun Gong and Yelong Shen and Jian Jiao and Haitao Zheng and Juntao Li and Zhongyu Wei and Jian Guo and Nan Duan and Weizhu Chen } ,
    journal   = { ArXiv } ,
    year      = { 2023 } ,
    volume    = { abs/2305.09515 } ,
    url       = { https://api.semanticscholar.org/CorpusID:258714669 }
}

 @article { Karras2022ElucidatingTD ,
    title   = { Elucidating the Design Space of Diffusion-Based Generative Models } ,
    author  = { Tero Karras and Miika Aittala and Timo Aila and Samuli Laine } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2206.00364 } ,
    url     = { https://api.semanticscholar.org/CorpusID:249240415 }
}

 @article { Liu2022FlowSA ,
    title   = { Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow } ,
    author  = { Xingchao Liu and Chengyue Gong and Qiang Liu } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2209.03003 } ,
    url     = { https://api.semanticscholar.org/CorpusID:252111177 }
}

 @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 }
}