Textual Inversion
Textual Inversion allows you to train a tiny part of the neural network on your own pictures, and use results when generating new ones.
The result of training is a .pt or a .bin file (former is the format used by original author, latter is by the diffusers library).
Put the embedding into the embeddings directory and use its filename in the prompt. You don't have to restart the program for this to work.
As an example, here is an embedding of Usada Pekora I trained on WD1.2 model, on 53 pictures (119 augmented) for 19500 steps, with 8 vectors per token setting.
Pictures it generates:
portrait of usada pekora
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 4077357776, Size: 512x512, Model hash: 45dee52b
You can combine multiple embeddings in one prompt:
portrait of usada pekora, mignon
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 4077357776, Size: 512x512, Model hash: 45dee52b
Be very careful about which model you are using with your embeddings: they work well with the model you used during training, and not so well on different models. For example, here is the above embedding and vanilla 1.4 stable diffusion model:
portrait of usada pekora
Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 4077357776, Size: 512x512, Model hash: 7460a6fa