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.
Running on Colab
Colab for training
Colab for inference
Running locally with PyTorch
Installing the dependencies
Before running the scripts, make sure to install the library's training dependencies:
Important
To make sure you can successfully run the latest versions of the example scripts, we highly recommend installing from source and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
Then cd in the example folder and run
pip install -r requirements.txt
And initialize an 🤗Accelerate environment with:
accelerate config
Cat toy example
You need to accept the model license before downloading or using the weights. In this example we'll use model version v1-5, so you'll need to visit its card, read the license and tick the checkbox if you agree.
You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to this section of the documentation.
Run the following command to authenticate your token
huggingface-cli login
If you have already cloned the repo, then you won't need to go through these steps.
Now let's get our dataset.Download 3-4 images from here and save them in a directory. This will be our training data.
Use ONNXRuntime to accelerate training
In order to leverage onnxruntime to accelerate training, please use textual_inversion.py
The command to train on custom data with onnxruntime:
export MODEL_NAME="runwayml/stable-diffusion-v1-5"
export DATA_DIR="path-to-dir-containing-images"
accelerate launch textual_inversion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_data_dir=$DATA_DIR \
--learnable_property="object" \
--placeholder_token="<cat-toy>" --initializer_token="toy" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=4 \
--max_train_steps=3000 \
--learning_rate=5.0e-04 --scale_lr \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--output_dir="textual_inversion_cat"
Please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.