autoregressive-diffusion-pytorch 0.2.1

Autoregressive Diffusion - Pytorch

Autoregressive Diffusion - Pytorch

Implementation of the architecture behind Autoregressive Image Generation without Vector Quantization in Pytorch

Official repository has been released here

oxford flowers at 96k steps

Install

$ pip install autoregressive-diffusion-pytorch

Usage

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

For images treated as a sequence of tokens (as in paper)

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

An images trainer

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()

Citations

@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}
}