diffusers/examples/research_projects/intel_opts/textual_inversion

Textual Inversion fine-tuning example

Textual inversion is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples. The textual_inversion.py script shows how to implement the training procedure and adapt it for stable diffusion.

Training with Intel Extension for PyTorch

Intel Extension for PyTorch provides the optimizations for faster training and inference on CPUs. You can leverage the training example "textual_inversion.py". Follow the instructions to get the model and dataset before running the script.

The example supports both single node and multi-node distributed training:

Single node training

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR="path-to-dir-containing-dicoo-images"

python textual_inversion.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATA_DIR \
  --learnable_property="object" \
  --placeholder_token="<dicoo>" --initializer_token="toy" \
  --seed=7 \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --max_train_steps=3000 \
  --learning_rate=2.5e-03 --scale_lr \
  --output_dir="textual_inversion_dicoo"

Note: Bfloat16 is available on Intel Xeon Scalable Processors Cooper Lake or Sapphire Rapids. You may not get performance speedup without Bfloat16 support.

Multi-node distributed training

Before running the scripts, make sure to install the library's training dependencies successfully:

python -m pip install oneccl_bind_pt==1.13 -f https://developer.intel.com/ipex-whl-stable-cpu

export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export DATA_DIR="path-to-dir-containing-dicoo-images"

oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh

python -m intel_extension_for_pytorch.cpu.launch --distributed \
  --hostfile hostfile --nnodes 2 --nproc_per_node 2 textual_inversion.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --train_data_dir=$DATA_DIR \
  --learnable_property="object" \
  --placeholder_token="<dicoo>" --initializer_token="toy" \
  --seed=7 \ 
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=1 \
  --max_train_steps=750 \
  --learning_rate=2.5e-03 --scale_lr \
  --output_dir="textual_inversion_dicoo"

The above is a simple distributed training usage on 2 nodes with 2 processes on each node. Add the right hostname or ip address in the "hostfile" and make sure these 2 nodes are reachable from each other. For more details, please refer to the user guide.

Reference

We publish a Medium blog on how to create your own Stable Diffusion model on CPUs using textual inversion. Try it out now, if you have interests.