自回归扩散 - Pytorch
在Pytorch中实现无向量量化的自回归图像生成背后的架构
官方代码库已在此处发布
96k步后的牛津花卉数据集
安装
$ pip install autoregressive-diffusion-pytorch
使用方法
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
对于将图像视为一系列标记的情况(如论文所述)
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
引用
@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}
}