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https://github.com/invoke-ai/InvokeAI
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71 lines
3.1 KiB
Markdown
71 lines
3.1 KiB
Markdown
# **Personalizing Text-to-Image Generation**
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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.
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**Training**
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To train, prepare a folder that contains images sized at 512x512 and execute the following:
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**WINDOWS**: As the default backend is not available on Windows, if you're using that platform, set the environment variable `PL_TORCH_DISTRIBUTED_BACKEND=gloo`
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```
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(ldm) ~/stable-diffusion$ python3 ./main.py --base ./configs/stable-diffusion/v1-finetune.yaml \
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-t \
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--actual_resume ./models/ldm/stable-diffusion-v1/model.ckpt \
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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
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input images for the training epoch samples, samples scaled for a
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sample of the prompt 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_: 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
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ctrl-c) before the heat death of the universe, when you find a low
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loss epoch or around ~5000 iterations. Note that you can set a fixed
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limit on the number of training steps by decreasing the "max_steps"
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option in configs/stable_diffusion/v1-finetune.yaml (currently set to
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4000000)
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**Running**
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Once the model is trained, specify the trained .pt or .bin file when
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starting dream using
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```
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(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py --embedding_path /path/to/embedding.pt --full_precision
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```
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Then, to utilize your subject at the dream prompt
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```
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dream> "a photo of *"
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```
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This also works with image2image
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```
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dream> "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 placeholder string in `configs/stable-diffusion/v1-finetune.yaml`) and combine LDM checkpoints using:
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```
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(ldm) ~/stable-diffusion$ python3 ./scripts/merge_embeddings.py \
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--manager_ckpts /path/to/first/embedding.pt /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 located at https://github.com/rinongal/textual_inversion Please see the repository and associated paper for details and limitations.
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