usage.md · adamelliotfields/diffusion at c5cf56651dc096b8e66bbaf883d29f7198c3cfc9

Usage

Enter a prompt and click Generate.

Prompting

Positive and negative prompts are embedded by 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 to learn more and read Civitai's guide on prompting for best practices.

Arrays

Arrays allow you to generate different images from a single prompt. For example, [[cat,corgi]] will expand into 2 separate prompts. Make sure Images is set accordingly (e.g., 2). Only works for the positive prompt. Inspired by Fooocus.

Embeddings

Select multiple negative textual inversion embeddings. Fast Negative and Bad Dream can be used standalone or together; Unrealistic Dream should be combined with one of the others:

<fast_negative>: all-purpose (default)
<bad_dream>: DreamShaper-style
<unrealistic_dream>: realistic add-on

Styles

Styles are prompt templates from twri's sdxl_prompt_styler Comfy node. Start with a subject like "cat", pick a style, and iterate from there.

Scale

Rescale up to 4x using Real-ESRGAN.

Models

Each model checkpoint has a different aesthetic:

lykon/dreamshaper-8: general purpose (default)
fluently/fluently-v4: general purpose merge
linaqruf/anything-v3-1: anime
prompthero/openjourney-v4: Midjourney-like
runwayml/stable-diffusion-v1-5: base
sg161222/realistic_vision_v5.1: photorealistic

Schedulers

Optionally, the Karras noise schedule can be used:

Image-to-Image

The 🖼️ Image tab enables the image-to-image pipeline. Either use the image input or select a generation from the gallery and then adjust the denoising strength. To disable, simply clear the image input (the x overlay button).

Denoising strength is essentially how much the generation will differ from the input image. A value of 0 will be identical to the original, while 1 will be a completely new image. You may want to also increase the number of inference steps.

Advanced

DeepCache

DeepCache (Ma et al. 2023) caches lower U-Net layers and reuses them every Interval steps:

1: no caching
2: more quality (default)
3: balanced
4: more speed

FreeU

FreeU (Si et al. 2023) re-weights the contributions sourced from the U-Net’s skip connections and backbone feature maps to potentially improve image quality.

Clip Skip

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

Tiny VAE

Enable madebyollin/taesd for almost instant latent decoding with a minor loss in detail. Useful for development.

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.