fix(docs): describe LoRA and Textual Inversion tokens in user guide
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@ -31,7 +31,12 @@ Please see [the server admin guide](server-admin.md) for details on how to confi
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- [Model sources](#model-sources)
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- [Model sources](#model-sources)
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- [Downloading models from Civitai](#downloading-models-from-civitai)
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- [Downloading models from Civitai](#downloading-models-from-civitai)
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- [Using a custom VAE](#using-a-custom-vae)
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- [Using a custom VAE](#using-a-custom-vae)
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- [Using and controlling Textual Inversions](#using-and-controlling-textual-inversions)
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- [Prompts](#prompts)
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- [General structure](#general-structure)
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- [Useful keywords](#useful-keywords)
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- [Extra network tokens](#extra-network-tokens)
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- [LoRA tokens](#lora-tokens)
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- [Textual Inversion tokens](#textual-inversion-tokens)
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- [Tabs](#tabs)
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- [Tabs](#tabs)
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- [Txt2img tab](#txt2img-tab)
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- [Txt2img tab](#txt2img-tab)
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- [Scheduler parameter](#scheduler-parameter)
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- [Scheduler parameter](#scheduler-parameter)
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@ -130,6 +135,10 @@ image from history if you don't like it.
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### Scheduler comparison
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### Scheduler comparison
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The Stable Diffusion pipeline can be run using different schedulers, which generally produce similar results but
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each have their own advantages. Some schedulers are faster than other or require fewer steps, especially the recent
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DEIS multistep and Euler Ancestral schedulers.
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- https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers#compare-schedulers
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- https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers#compare-schedulers
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- https://i.imgur.com/2pQPgf0.jpeg
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- https://i.imgur.com/2pQPgf0.jpeg
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@ -301,10 +310,49 @@ Some common VAE models include:
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- https://huggingface.co/stabilityai/sd-vae-ft-mse
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- https://huggingface.co/stabilityai/sd-vae-ft-mse
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- https://huggingface.co/stabilityai/sd-vae-ft-mse-original
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- https://huggingface.co/stabilityai/sd-vae-ft-mse-original
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### Using and controlling Textual Inversions
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## Prompts
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You can use a Textual Inversion along with a diffusion model by giving one or more of the tokens from the inversion
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### General structure
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model. Some Textual Inversions only have a single layer and some have 75 or more.
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TODO
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### Useful keywords
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TODO
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### Extra network tokens
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You can blend extra networks with the diffusion model using `<type:name:weight>` tokens.
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#### LoRA tokens
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You can blend one or more [LoRA embeddings](TODO: whitepaper) with the ONNX diffusion model using a `lora` token:
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```none
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<lora:name:0.5>
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```
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LoRA models must be placed in the `models/lora` directory.
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The type of network, name, and weight must be separated by colons. The LoRA name must be alphanumeric and must not
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contain any special characters.
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#### Textual Inversion tokens
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You can blend one or more [Textual Inversions](TODO: whitepaper) with the ONNX diffusion model using the `inversion`
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token:
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```none
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<inversion:autumn:1.0>
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```
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Textual Inversion embeddings must be placed in the `models/inversion` directory.
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The type of network, name, and weight must be separated by colons. The Textual Inversion name must be alphanumeric
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and must not contain any special characters.
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Once the Textual Inversion has been blended, you can activate some or all of its layers using the trained token(s)
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in your prompt. Some Textual Inversions only have a single layer and some have 75 or more.
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You can provide more than one of the numbered layer tokens using the `base-{X,Y}` range syntax in your prompt. This
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You can provide more than one of the numbered layer tokens using the `base-{X,Y}` range syntax in your prompt. This
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uses the Python range rules, so `X` is inclusive and `Y` is not. The range `autumn-{0,5}` will be expanded into the
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uses the Python range rules, so `X` is inclusive and `Y` is not. The range `autumn-{0,5}` will be expanded into the
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