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TEXTUAL_INVERSION

:material-file-document-plus-outline: TEXTUAL_INVERSION

Personalizing Text-to-Image Generation

You may personalize the generated images to provide your own styles or objects by training a new LDM checkpoint and introducing a new vocabulary to the fixed model as a (.pt) embeddings file. Alternatively, you may use or train HuggingFace Concepts embeddings files (.bin) from https://huggingface.co/sd-concepts-library and its associated notebooks.

Training

To train, prepare a folder that contains images sized at 512x512 and execute the following:

WINDOWS

As the default backend is not available on Windows, if you're using that platform, set the environment variable PL_TORCH_DISTRIBUTED_BACKEND to gloo

python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
                  --actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
                  -t \
                  -n my_cat \
                  --gpus 0 \
                  --data_root D:/textual-inversion/my_cat \
                  --init_word 'cat'

During the training process, files will be created in /logs/[project][time][project]/ where you can see the process.

Conditioning contains the training prompts inputs, reconstruction the input images for the training epoch samples, samples scaled for a sample of the prompt and one with the init word provided.

On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.

!!! note

According to the associated paper, the optimal number of
images is 3-5. Your model may not converge if you use more images than
that.

Training will run indefinitely, but you may wish to stop it (with ctrl-c) before the heat death of the universe, when you find a low loss epoch or around ~5000 iterations. Note that you can set a fixed limit on the number of training steps by decreasing the "max_steps" option in configs/stable_diffusion/v1-finetune.yaml (currently set to 4000000)

Run the Model

Once the model is trained, specify the trained .pt or .bin file when starting invoke using

python3 ./scripts/invoke.py --embedding_path /path/to/embedding.pt

Then, to utilize your subject at the invoke prompt

invoke> "a photo of *"

This also works with image2image

invoke> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4

For .pt files it's also possible to train multiple tokens (modify the placeholder string in configs/stable-diffusion/v1-finetune.yaml) and combine LDM checkpoints using:

python3 ./scripts/merge_embeddings.py \
        --manager_ckpts /path/to/first/embedding.pt \
        [</path/to/second/embedding.pt>,[...]] \
        --output_path /path/to/output/embedding.pt

Credit goes to rinongal and the repository

Please see the repository and associated paper for details and limitations.