Stable Diffusion trainer with scalable dataset size and hardware usage.
[!] IN EARLY DEVELOPMENT, CONFIGS AND ARGUMENTS SUBJECT TO BREAKING CHANGES
- Can run with 10G or less VRAM without losing speed thanks to xformers memory efficient attention and int8 optimizers.
- Aspect Ratio Bucketing
- DreamBooth
- CLIP skip
- WandB logging
Linux is recommended. (If you care to install bitsandbytes on Windows)
Python 3.10. Will not work on 3.7.
Torch 1.13 and CUDA 11.6. Match exact version is recommended but not required.
xformers is required for efficient VRAM usage. Easiest way to install it
is conda install -c "xformers/label/dev" xformers.
WandB (pip install wandb) is recommended for logging stats and previews.
pip install -r requirements.txtDocumentation: configs/README.md.
(Link)
configs/native.yaml (for native training) and configs/dreambooth.yaml (for DreamBooth) provided as examples.
If you are running native training, proceed to the next step.
If you are running DreamBooth, run this to generate class (regularization) images:
python gen_class_imgs.py --config configs/your_config.yamlThen run the training:
python train.py --config configs/your_config.yamlNote although the checkpoints have .ckpt extension, they are NOT directly usable to interfaces based on
the official SD code base
like WebUI. To convert them into SD checkpoints:
python convert_to_sd.py PATH_TO_THE_CKPT OUTPUTDIR --no-text-encoder --unet-dtype fp16--no-text-encoder --unet-dtype fp16 results a ~2GB checkpoint, containing fp16 UNet and fp32 VAE weights, WebUI
supports loading that. For further reducing checkpoint size to ~1.6GB if target clients have external VAE already,
add --no-vae to remove VAE weights from checkpoint, leaving fp16 UNet weights only.
If you are not using WebUI and having issues, remove --no-text-encoder.
You may change trainer.accelerator.
(Docs)
Check out the wiki. Contains some information for training efficiently.