ml_mdm
is a python package for efficiently training high quality text-to-image diffusion models โ brought to the public by Luke Carlson, Jiatao Gu, Shuangfei Zhai, and Navdeep Jaitly.
This software project accompanies the research paper, Matryoshka Diffusion Models.
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Josh Susskind, Navdeep Jaitly
Section | Description |
---|---|
Introduction | A brief overview of Matryoshka Diffusion Models |
Installation | Start training models and generating samples with ml_mdm |
Pretrained Models | Links to download our pretrained models (64, 356, 1024) |
Web Demo | Generate images with our web UI |
Codebase Structure | An overview of the python module |
Concepts | Core concepts and design principles. |
Tutorial | Step-by-step training of an MDM model on CC12m |
Diffusion models are the de facto approach for generating high-quality images and videos, but learning high-dimensional models remains a formidable task due to computational and optimization challenges.
ml_mdm
is an end-to-end framework for high-resolution image and video synthesis โ it is named after our technique: Matryoshka Diffusion Models.
Remarkably, we can train a single pixel-space model at resolutions of up to 1024x1024 pixels, demonstrating strong zero-shot generalization using the CC12M dataset, which contains only 12 million images.
The default installation dependencies, as defined in the pyproject.toml
, are selected so that you can install this library even on a CPU only machine.
Users have run this codebase with Python 3.9,3.10 and cuda_12, cuda-11.8
> pip install .
Developers should set up pre-commit
as well with pre-commit install
.
> pytest # will run all test cases - including ones that require a gpu
> pytest -m "not gpu" # run test cases that can work with just cpu
We've uploaded model checkpoints to:
- https://docs-assets.developer.apple.com/ml-research/models/mdm/flickr64/vis_model.pth
- https://docs-assets.developer.apple.com/ml-research/models/mdm/flickr256/vis_model.pth
- https://docs-assets.developer.apple.com/ml-research/models/mdm/flickr1024/vis_model.pth
Note: We are releasing models that were trained on 50M text-image pairs collected from Flickr. In this repo, we provide scripts for downloading CC12M and configs for training equivalent models on CC12M data.
Feel free to download the models or skip further down to train your own. Once a pretrained model is downloaded locally, you can use it in our web demo, pass it as an argument to training, sampling, and more.
export ASSET_PATH=https://docs-assets.developer.apple.com/ml-research/models/mdm
curl $ASSET_PATH/flickr64/vis_model.pth --output vis_model_64x64.pth
curl $ASSET_PATH/flickr256/vis_model.pth --output vis_model_256x256.pth
curl $ASSET_PATH/flickr1024/vis_model.pth --output vis_model_1024x1024.pth
You can run your own instance of the web demo (after downloading the checkpoints) with this command:
torchrun --standalone --nproc_per_node=1 ml_mdm/clis/generate_sample.py --port $YOUR_PORT
module | description |
---|---|
ml_mdm.models |
The core model implementations |
ml_mdm.diffusion |
Model pipelines, for example DDPM |
ml_mdm.config |
Connects configuration dataclasses with associated models, pipelines, and clis using simple parsing |
ml_mdm.clis |
All command line tools in the project, the most relevant being train_parallel.py |
tests/ |
Unit tests and sample training files |
In the ml_mdm.models
submodule, we've open sourced our implementations of:
- U-Nets
- Nested U-Nets
ml_mdm.config
contains the core configuration and cli logic. Many models, clis, and functions in this codebase are configured by passing in a dataclass
object. We use SimpleParsing to dynamically create clis and allow passing in yaml config
representations with --config_path
.
In essence,
simple_parsing
will convert all passed cli arguments and yaml files into clean configuration classes likeml_mdm.reader.ReaderConfig
,ml_mdm.diffusion.DiffusionConfig
.
Once you've installed ml_mdm
, download these checkpoints into the repo's directory.
curl https://docs-assets.developer.apple.com/ml-research/models/mdm/flickr64/vis_model.pth --output vis_model_64x64.pth
curl https://docs-assets.developer.apple.com/ml-research/models/mdm/flickr256/vis_model.pth --output vis_model_256x256.pth
The web demo will load each model with a corresponding configuration:
vis_model_64x64.pth
will be loaded with the settings fromconfigs/models/cc12m_64x64.yaml
vis_model_256x256.pth
will be loaded with the settings fromconfigs/models/cc12m_256x256.yaml
vis_model_1024x1024.pth
will be loaded with the settings fromconfigs/models/cc12m_1024x1024.yaml
In the demo, you can change a variety of settings and peek into the internals of the model. Set the port you'd like to use by swapping in $YOUR_PORT
and then run:
torchrun --standalone --nproc_per_node=1 ml_mdm/clis/generate_sample.py --port $YOUR_PORT
If you just want to step through the process of training a model and running a pipeline without downloading a large dataset, we've put together a minimal example for you. It uses the dummy data from tests/test_files/
Feel free to try changing a variety of --args either directly in the cli or by editing the config yaml file
torchrun --standalone --nproc_per_node=1 ml_mdm/clis/train_parallel.py \
--file-list=tests/test_files/sample_training_0.tsv \
--multinode=0 \
--output-dir=outputs --config_path configs/models/cc12m_64x64.yaml \
-num_diffusion_steps=10 \
--num-training-steps=10
You should see a outputs/vis_model_000100.pth
file. Now lets do something a bit more meaningful:
(OPTIONAL) Download the first 1K files of CC12m with this sample argument
The script is based on img2dataset's CC12M script.
curl https://storage.googleapis.com/conceptual_12m/cc12m.tsv | head -n 1000 > cc12m_index.tsv
# Add headers to the file
sed -i '1s/^/url\tcaption\n/' cc12m_index.tsv
Note: if you want all of cc12m, remove
| head -n 1000
from the call
Then prepare and split into train/validation
This script requires
img2dataset
, either runpip install '.[data_prep]'
or justpip install img2dataset
python3 -m ml_mdm.clis.scrape_cc12m \
--cc12m_index cc12m_index.tsv \
--cc12m_local_dir cc12m_download
After running this command you will see the following files:
training.0.tsv # train index file
validation.tsv # validation index file
cc12m_download/
00000.parquet 00000.tar 00000.tsv 00000_stats.json validation.tsv
00001.parquet ....
Now that we have our training file, we can select a model config and pass any additional training arguments:
# Modify torchrun arguments to fit your GPU setup
torchrun --standalone --nproc_per_node=8 ml_mdm/clis/train_parallel.py \
--file-list=training_0.tsv \
--multinode=0 --output-dir=/mnt/data/outputs \
--config_path configs/models/cc12m_64x64.yaml \
--num-training-steps=100 --warmup-steps 10
Note:
configs/models/cc12m_64x64.yaml
contains many more arguments, check it out for more details.
If you've downloaded a pretrained model, you can set the
--pretrained-vision-file
argument to point to its location on disk
Once training completes, you'll find the model in the folder defined by the --output-dir argument:
2024-07-22:17:58:46,649 INFO [model_ema.py:33] Saving EMA model file: /mnt/data/outputs/vis_model_000100.pth
2024-07-22:17:58:47,448 INFO [unet.py:794] Saving model file: /mnt/data/outputs/vis_model_noema_000100.pth
Now that we have a trained model, we can generate samples from the diffusion model:
torchrun --standalone --nproc_per_node=1 ml_mdm/clis/generate_batch.py \
--config_path configs/models/cc12m_64x64.yaml \
--min-examples 3 --test-file-list validation.tsv \
--sample-image-size 64 --model-file /mnt/data/outputs/vis_model_000100.pth
If you want to skip the training step, you can update
--model-file
to point to one of our pretrained models
For long term storage, you can optionally upload your data to s3://{your_bucket}/datasets/{datasetname}/*.[tar,tsv]
.
Then update configs/datasets/cc12m.yaml
to point to your s3 paths.
# configs/datasets/cc12m.yaml
train:
files:
- s3://mlx/datasets/cc12m-64x64/images_00.*.tsv
eval:
files:
- s3://mlx/datasets/cc12m-64x64/validation.tsv
# configs/datasets/reader_config.yaml
reader_config:
append_eos: true
bucket: ${your_bucket} # add your s3 bucket
endpoint_url: None # boto will automatically infer the endpoint
Then you can use our dataset download helper:
python -m ml_mdm.clis.download_tar_from_index \
--dataset-config-file configs/datasets/cc12m.yaml \
--subset train --download_tar
python -m ml_mdm.clis.download_tar_from_index \
--dataset-config-file configs/datasets/cc12m.yaml \
--subset eval --download_tar
Take a look at configs/datasets/cc12m.yaml
.
The code allows for multiple regular expressions to be provided. Keep in mind that the regular expressions are not globs -- they are regular expressions from the python re library. So if you wanted to use only 100 of the 1000 tar files in WebImage for training you can do the following:
train:
files:
- s3://mlx/datasets/example-dataset-100M_64px/example-dataset-100M-00[0-1]..-[0-9]*-of-01000.tsv
eval:
files:
- s3://mlx/datasets/example-dataset-100M_64px/validation.tsv
You can also mix and match the files. So if you wanted to merge CC12m and imagenet you could create a new yaml file with the following contents:
train:
files:
- s3://mlx/datasets/imagenet-64px/imagenet-train-000??-of-00100.tsv
- s3://mlx/datasets/cc12m-64x64/images_00.*.tsv
eval:
files:
- s3://mlx/datasets/cc12m-64x64/validation.tsv
The S3 Bucket contains a series of files in this format, take a look at ml_mdm/clis/scrape_cc12m.py
to generate your own.
2023-04-01 01:31:30 36147200 images_00000.tar
2023-05-10 11:34:49 1108424 images_00000.tsv
2023-04-01 01:31:26 36454400 images_00001.tar
2023-05-10 11:34:49 1109588 images_00001.tsv
2023-04-01 01:31:53 36116480 images_00002.tar
...
Minimal representations of these files can be found at tests/test_files/
.
If you find our work useful, please consider citing us as:
@misc{gu2023matryoshkadiffusionmodels,
title={Matryoshka Diffusion Models},
author={Jiatao Gu and Shuangfei Zhai and Yizhe Zhang and Josh Susskind and Navdeep Jaitly},
year={2023},
eprint={2310.15111},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2310.15111},
}