⚠️ Warning: The scripts in this repository have the potential to damage your training data. Always maintain backups before proceeding.
SimpleTuner is a repository dedicated to a set of experimental scripts designed for training optimization. The project is geared towards simplicity, with a focus on making the code easy to read and understand. This codebase serves as a shared academic exercise, and contributions are welcome.
- Simplicity: Aiming to have good default settings for most use cases, so less tinkering is required.
- Versatility: Designed to handle a wide range of image quantities - from small datasets to extensive collections.
- Cutting-Edge Features: Only incorporates features that have proven efficacy, avoiding the addition of untested options.
Please fully explore this README before embarking on the tutorial, as it contains vital information that you might need to know first.
For a quick start without reading the full documentation, you can use the Quick Start guide.
For memory-constrained systems, see the DeepSpeed document which explains how to use 🤗Accelerate to configure Microsoft's DeepSpeed for optimiser state offload.
- Multi-GPU training
- Image and caption features (embeds) are cached to the hard drive in advance, so that training runs faster and with less memory consumption
- Aspect bucketing: support for a variety of image sizes and aspect ratios, enabling widescreen and portrait training.
- Refiner LoRA or full u-net training for SDXL
- Most models are trainable on a 24G GPU, or even down to 16G at lower base resolutions.
- LoRA training for PixArt, SDXL, SD3, and SD 2.x that uses less than 16G VRAM; AuraFlow uses less than 24G VRAM
- DeepSpeed integration allowing for training SDXL's full u-net on 12G of VRAM, albeit very slowly.
- Optional EMA (Exponential moving average) weight network to counteract model overfitting and improve training stability. Note: This does not apply to LoRA.
- Train directly from an S3-compatible storage provider, eliminating the requirement for expensive local storage. (Tested with Cloudflare R2 and Wasabi S3)
- For only SDXL and SD 1.x/2.x, full ControlNet model training (not ControlLoRA or ControlLite)
- Training Mixture of Experts for lightweight, high-quality diffusion models
- Webhook support for updating eg. Discord channels with your training progress, validations, and errors
- Integration with the Hugging Face Hub for seamless model upload and nice automatically-generated model cards.
SimpleTuner has extensive training integration with PixArt Sigma - both the 600M & 900M models load without any fuss.
- Text encoder training is not supported, as T5 is enormous.
- LoRA and full tuning both work as expected
- ControlNet training is not yet supported
- Two-stage PixArt training support (see: MIXTURE_OF_EXPERTS)
See the PixArt Quickstart guide to start training.
Stable Diffusion 2.1 is known for difficulty during fine-tuning, but this doesn't have to be the case. Related features in SimpleTuner include:
- Training only the text encoder's later layers
- Enforced zero SNR on the terminal timestep instead of offset noise for clearer images.
- The use of EMA (exponential moving average) during training to ensure we do not "fry" the model.
- The ability to train on multiple datasets with different base resolutions in each, eg. 512px and 768px images simultaneously
- LoRA and full finetuning are supported as usual.
- ControlNet is not yet implemented.
- Certain features such as segmented timestep selection and Compel long prompt weighting are not yet supported.
- Parameters have been optimised to get the best results, validated through from-scratch training of SD3 models
See the Stable Diffusion 3 Quickstart to get going.
AuraFlow is a novel, open-source implementation of a flow-matching text-to-image model using a simplified architecture compared to SD3, with a greater number of parameters.
Currently, AuraFlow v0.1 has limited support for SimpleTuner:
- All limitations that apply to Stable Diffusion 3 also apply to AuraFlow
- LoRA is currently the only viable method of AuraFlow training
An SDXL-based model with ChatGLM (General Language Model) 6B as its text encoder, doubling the hidden dimension size and substantially increasing the level of local detail included in the prompt embeds.
Kolors support is almost as deep as SDXL, minus ControlNet training support.
EMA (exponential moving average) weights are a memory-heavy affair, but provide fantastic results at the end of training. Options like --ema_cpu_only can improve this situation by loading EMA weights onto the CPU and then keeping them there.
Without EMA, more care must be taken not to drastically change the model leading to "catastrophic forgetting" through the use of regularisation data.
- NVIDIA - pretty much anything 3090 and up is a safe bet. YMMV.
- AMD - SDXL LoRA and UNet are verified working on a 7900 XTX 24GB. Lacking
xformers, it will likely use more memory than Nvidia equivalents - Apple - LoRA and full u-net tuning are tested to work on an M3 Max with 128G memory, taking about 12G of "Wired" memory and 4G of system memory for SDXL.
- You likely need a 24G or greater machine for machine learning with M-series hardware due to the lack of memory-efficient attention.
- A100-80G (EMA, large batches, LoRA @ insane batch sizes)
- A6000-48G (EMA@768px, no EMA@1024px, LoRA @ high batch sizes)
- A100-40G (no EMA@1024px, no EMA@768px, EMA@512px, LoRA @ high batch sizes)
- 4090-24G (no EMA@1024px, batch size 1-4, LoRA @ medium-high batch sizes)
- 4080-12G (LoRA @ low-medium batch sizes)
- A100-40, A40, A6000 or better (EMA, 1024px training)
- NVIDIA RTX 4090 or better (24G, no EMA)
- NVIDIA RTX 4080 or better (LoRA only)
This model is very large; it will require more resources to train than any other, incurring a substantial hardware cost.
- Full tuning will OOM at a batch size of 1 on a single 80G GPU. A system with 8x A100-80G (SXM4) is a recommended minimum for FSDP (DeepSpeed ZeRO Stage 2) training.
- LoRA training will OOM at a batch size of 1 on a single 16G GPU. A system with 1x 24G is required, with a 48G GPU being an ideal size.
ubuntu.sh- This is a basic "installer" that makes it quick to deploy on a Vast.ai instance. It might not work for every single container image.train.sh- The main training script for SDXL.config/config.env.example- These are training parameters, you should copy toconfig/config.env
For more information about the associated toolkit distributed with SimpleTuner, refer to the toolkit documentation.
Detailed setup information is available in the installation documentation.
Enable debug logs for a more detailed insight by adding export SIMPLETUNER_LOG_LEVEL=DEBUG to your environment file.
For performance analysis of the training loop, setting SIMPLETUNER_TRAINING_LOOP_LOG_LEVEL=DEBUG will have timestamps that hilight any issues in your configuration.
For a comprehensive list of options available, consult this documentation.