Skip to main content

Autoregressive Diffusion - Pytorch

Project description

Autoregressive Diffusion - Pytorch

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

Official repository has been released here

Alternative route

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()

For an improvised version using flow matching, just import ImageAutoregressiveFlow and AutoregressiveFlow instead

The rest is the same

ex.

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()

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}
}
@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}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

autoregressive_diffusion_pytorch-0.2.8.tar.gz (682.7 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file autoregressive_diffusion_pytorch-0.2.8.tar.gz.

File metadata

File hashes

Hashes for autoregressive_diffusion_pytorch-0.2.8.tar.gz
Algorithm Hash digest
SHA256 b644ed792b1e6a7bc5d4efa6dee82302aae60bfe00d5c40a418494665d06c839
MD5 08972ccf0bcf1f9d10d4a5eddacb79de
BLAKE2b-256 704f6247f2a8e0cb6ed4d4c8ec214b1b1f96e6ebca56b53edc605ed309ed3b2d

See more details on using hashes here.

File details

Details for the file autoregressive_diffusion_pytorch-0.2.8-py3-none-any.whl.

File metadata

File hashes

Hashes for autoregressive_diffusion_pytorch-0.2.8-py3-none-any.whl
Algorithm Hash digest
SHA256 16adbb8018476fb32cd095de235043f660d22b874e671ac18ccd83be6d2d10a1
MD5 d905acb7c3e62996779d563129fa5957
BLAKE2b-256 02d8d2f487d9049eacdecae810a9f1f8c0f37e4b06984dac076f2497ae00a0db

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page