This repo provides code for benchmarking Stable Diffusion using Streaming, Composer, and MosaicML Cloud. The benchmarking results are presented in this blog post, but the table is duplicated below.
In this repo, you will find:
benchmark.py- defines the Stable DiffusionComposerModeland the ComposerTrainer.data.py- defines the MosaicML Stremaing LAION dataset and a synthetic dataset as an alternative to streaming data.ema.py- a memory-efficient version of Composer's EMA algorithmmcloud.yaml- examples of how to use MosaicML Cloud to launch a training run.
If you are interested in using the MosaicML Cloud, sign up for a demo here!
Install required dependencies using pip install -r requirements.txt
If you would like to use xFormers install it using (we specify a commit we know will work):
pip install -v -U git+https://github.com/facebookresearch/xformers.git@3df785ce54114630155621e2be1c2fa5037efa27#egg=xformersTo benchmark without using a streaming dataset:
composer benchmark.py --use_ema --use_synth_data --device_train_microbatch_size 4device_train_microbatch_size should be 4 when using a NVIDIA 40GB A100 GPUs and xFormers. If you are not using xFormers, device_train_microbatch_size should be 2. If using a smaller GPU, adjust device_train_microbatch_size as needed
To log benchmark results, set up a Weights and Biases account, then specify the --wandb_name and --wandb_project arguments.
If you want to benchmark using a streaming dataset, specify the --remote argument:
composer benchmark.py --use_ema --device_train_microbatch_size 4 --remote s3://my-bucket/laion/mdsIf you run into any problems with the code, please file Github issues directly to this repo.
If you want train diffusion models on MosaicML Cloud, schedule a demo online or email us at demo@mosaicml.com