https://github.com/lucidrains/autoregressive-diffusion-pytorch
Implementation of Autoregressive Diffusion in Pytorch
https://github.com/lucidrains/autoregressive-diffusion-pytorch
artificial-intelligence autoregressive-diffusion deep-learning
Last synced: 9 months ago
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Implementation of Autoregressive Diffusion in Pytorch
- Host: GitHub
- URL: https://github.com/lucidrains/autoregressive-diffusion-pytorch
- Owner: lucidrains
- License: mit
- Created: 2024-07-23T16:22:47.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-03T16:23:30.000Z (over 1 year ago)
- Last Synced: 2025-05-31T06:48:09.004Z (about 1 year ago)
- Topics: artificial-intelligence, autoregressive-diffusion, deep-learning
- Language: Python
- Homepage:
- Size: 1.1 MB
- Stars: 382
- Watchers: 12
- Forks: 11
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 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
```bash
$ pip install autoregressive-diffusion-pytorch
```
## Usage
```python
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)
```python
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
```python
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.
```python
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
```bibtex
@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}
}
```
```bibtex
@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}
}
```
```bibtex
@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}
}
```
```bibtex
@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}
}
```
```bibtex
@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}
}
```