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98fe044dee
- rename dream.py to invoke.py - create a compatibility script named dream.py that execs() invoke.py - redo documentation - change help message in args - this does **not** rename the libraries, which are still ldm.dream.util, etc
92 lines
2.9 KiB
Markdown
92 lines
2.9 KiB
Markdown
---
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title: TEXTUAL_INVERSION
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---
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# :material-file-document-plus-outline: TEXTUAL_INVERSION
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## **Personalizing Text-to-Image Generation**
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You may personalize the generated images to provide your own styles or objects
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by training a new LDM checkpoint and introducing a new vocabulary to the fixed
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model as a (.pt) embeddings file. Alternatively, you may use or train
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HuggingFace Concepts embeddings files (.bin) from
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<https://huggingface.co/sd-concepts-library> and its associated notebooks.
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## **Training**
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To train, prepare a folder that contains images sized at 512x512 and execute the
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following:
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### WINDOWS
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As the default backend is not available on Windows, if you're using that
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platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND` to `gloo`
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```bash
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python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
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--actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
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-t \
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-n my_cat \
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--gpus 0 \
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--data_root D:/textual-inversion/my_cat \
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--init_word 'cat'
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```
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During the training process, files will be created in
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`/logs/[project][time][project]/` where you can see the process.
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Conditioning contains the training prompts inputs, reconstruction the input
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images for the training epoch samples, samples scaled for a sample of the prompt
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and one with the init word provided.
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On a RTX3090, the process for SD will take ~1h @1.6 iterations/sec.
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!!! note
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According to the associated paper, the optimal number of
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images is 3-5. Your model may not converge if you use more images than
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that.
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Training will run indefinitely, but you may wish to stop it (with ctrl-c) before
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the heat death of the universe, when you find a low loss epoch or around ~5000
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iterations. Note that you can set a fixed limit on the number of training steps
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by decreasing the "max_steps" option in
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configs/stable_diffusion/v1-finetune.yaml (currently set to 4000000)
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## **Run the Model**
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Once the model is trained, specify the trained .pt or .bin file when starting
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invoke using
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```bash
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python3 ./scripts/invoke.py --embedding_path /path/to/embedding.pt
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```
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Then, to utilize your subject at the invoke prompt
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```bash
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invoke> "a photo of *"
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```
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This also works with image2image
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```bash
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invoke> "waterfall and rainbow in the style of *" --init_img=./init-images/crude_drawing.png --strength=0.5 -s100 -n4
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```
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For .pt files it's also possible to train multiple tokens (modify the
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placeholder string in `configs/stable-diffusion/v1-finetune.yaml`) and combine
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LDM checkpoints using:
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```bash
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python3 ./scripts/merge_embeddings.py \
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--manager_ckpts /path/to/first/embedding.pt \
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[</path/to/second/embedding.pt>,[...]] \
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--output_path /path/to/output/embedding.pt
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```
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Credit goes to rinongal and the repository
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Please see [the repository](https://github.com/rinongal/textual_inversion) and
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associated paper for details and limitations.
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