how to train embeddings just on earrings and clothes #808
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Can anyone help me with creating embeddings. I want to train 3 separate embedding on 3 subjects. (or is it possible to train on all 3, that would be awesome) I don't know how to tell Automatic1111 to choose only the dress from the photo and not jewelry, choose just earrings and nothing else.
when we call dress, it should apply only dress type, and similarly with jewelries and earrings. |
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Replies: 2 comments 10 replies
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An embedding is basically an embedded txt2img macro. It works completely on the txt2img layer and the description should be the opposite of how you normally create a description for a model (for example). From personal experience, you may need to break this up into multiple embeddings. If you are all right with that, then describe every detail in the image except what you want. The embedding needs to infer (learn from what is not being described) what is missing from the images and it will recreate what is missing using txt2img. Examples
Now if you instead say "A woman wearing a blue sapphire princess cut necklace on her neck" the txt2img macro will ignore the necklace and never search or learn about it. Because you already included it in the txt2img prompt. Anything you include in the prompt will not be included in the embedding and the embedding will assume the user will need to type that every time as part of the text. It will be completely ignored for training. |
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:) Thank you very much for a detailed description @Ackerlight . Sorry for delayed reply. I didn't had access to system from few days. |
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An embedding is basically an embedded txt2img macro. It works completely on the txt2img layer and the description should be the opposite of how you normally create a description for a model (for example).
From personal experience, you may need to break this up into multiple embeddings. If you are all right with that, then describe every detail in the image except what you want. The embedding needs to infer (learn from what is not being described) what is missing from the images and it will recreate what is missing using txt2img.
Examples