## Usage

Enter a prompt and click `Generate`.

### Prompting

Positive and negative prompts are embedded by [Compel](https://github.com/damian0815/compel) for weighting. You can use a float or +/-. For example:
* `man, portrait, blue+ eyes, close-up`
* `man, portrait, (blue)1.1 eyes, close-up`
* `man, portrait, (blue eyes)-, close-up`
* `man, portrait, (blue eyes)0.9, close-up`

Note that `++` is `1.1^2` (and so on). See [syntax features](https://github.com/damian0815/compel/blob/main/doc/syntax.md) to learn more and read [Civitai](https://civitai.com)'s guide on [prompting](https://education.civitai.com/civitais-prompt-crafting-guide-part-1-basics/) for best practices.

#### Negative Prompt

Start with a [textual inversion](https://huggingface.co/docs/diffusers/en/using-diffusers/textual_inversion_inference) embedding:

* [`<bad_prompt>`](https://civitai.com/models/55700/badprompt-negative-embedding)
* [`<negative_hand>`](https://civitai.com/models/56519/negativehand-negative-embedding)
* [`<fast_negative>`](https://civitai.com/models/71961/fast-negative-embedding-fastnegativev2)
* [`<bad_dream>`](https://civitai.com/models/72437?modelVersionId=77169)
* [`<unrealistic_dream>`](https://civitai.com/models/72437?modelVersionId=77173)

And iterate from there. You can use weighting in the negative prompt as well.

#### Arrays

Arrays allow you to generate different images from a single prompt. For example, `man, [[blue,blue+,blue++]] eyes` will expand into 3 separate prompts. Make sure `Images` is set accordingly (e.g., 3). Only works for the positive prompt. Inspired by [Fooocus](https://github.com/lllyasviel/Fooocus/pull/1503).

When using arrays, you should disable `Autoincrement` so the same seed is used for each generation.

### Models

* [lykon/dreamshaper-8](https://huggingface.co/Lykon/dreamshaper-8): general purpose (default)
* [fluently/fluently-v4](https://huggingface.co/fluently/Fluently-v4): general purpose merge
* [linaqruf/anything-v3-1](https://huggingface.co/linaqruf/anything-v3-1): anime
* [prompthero/openjourney-v4](https://huggingface.co/prompthero/openjourney-v4): Midjourney-like
* [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5): base
* [sg161222/realistic_vision_v5.1](https://huggingface.co/SG161222/Realistic_Vision_V5.1_noVAE): photorealistic

#### Schedulers

All are based on [k_diffusion](https://github.com/crowsonkb/k-diffusion) except [DEIS](https://github.com/qsh-zh/deis) and [DPM++](https://github.com/LuChengTHU/dpm-solver). Optionally, the [Karras](https://arxiv.org/abs/2206.00364) noise schedule can be used:

* [DEIS 2M](https://huggingface.co/docs/diffusers/en/api/schedulers/deis) (default)
* [DPM++ 2M](https://huggingface.co/docs/diffusers/en/api/schedulers/multistep_dpm_solver)
* [DPM2 a](https://huggingface.co/docs/diffusers/api/schedulers/dpm_discrete_ancestral)
* [Euler a](https://huggingface.co/docs/diffusers/en/api/schedulers/euler_ancestral)
* [Heun](https://huggingface.co/docs/diffusers/api/schedulers/heun)
* [LMS](https://huggingface.co/docs/diffusers/api/schedulers/lms_discrete)
* [PNDM](https://huggingface.co/docs/diffusers/api/schedulers/pndm)

### Advanced

#### DeepCache

[DeepCache](https://github.com/horseee/DeepCache) (Ma et al. 2023) caches lower UNet layers and reuses them every `Interval` steps:
* `1`: no caching
* `2`: more quality (default)
* `3`: balanced
* `4`: more speed

#### T-GATE

[T-GATE](https://github.com/HaozheLiu-ST/T-GATE) (Zhang et al. 2024) caches self and cross attention computations up to `Step`. Afterwards, attention is no longer computed and the cache is used, resulting in a noticeable speedup. Defaults to `20`.

#### ToME

[ToMe](https://arxiv.org/abs/2303.17604) (Bolya & Hoffman 2023) reduces the number of tokens processed by the model. Set `Ratio` to the desired reduction factor. ToMe's impact is more noticeable on larger images.

#### Tiny VAE

Enable [madebyollin/taesd](https://github.com/madebyollin/taesd) for almost instant latent decoding with a minor loss in detail. Useful for development.

#### Clip Skip

When enabled, the last CLIP layer is skipped. This _can_ improve image quality with anime models.

#### Prompt Truncation

When enabled, prompts will be truncated to CLIP's limit of 77 tokens. By default this is disabled, so Compel will chunk prompts into segments rather than cutting them off.