DOCS.md

Usage

TL;DR: Enter a prompt or roll the 🎲 and press Generate.

Prompting

Positive and negative prompts are embedded by Compel for weighting. See syntax features to learn more.

Use + or - to increase the weight of a token. The weight grows exponentially when chained. For example, blue+ means 1.1x more attention is given to blue, while blue++ means 1.1^2 more, and so on. The same applies to -.

Groups of tokens can be weighted together by wrapping in parantheses and multiplying by a float between 0 and 2. For example, (masterpiece, best quality)1.2 will increase the weight of both masterpiece and best quality by 1.2x.

This is the same syntax used in InvokeAI and it differs from A1111:

Compel	A1111
blue++	((blue))
blue--	[[blue]]
(blue)1.2	(blue:1.2)
(blue)0.8	(blue:0.8)

Models

Some require specific parameters to get the best results, so check the model's link for more information:

• Lykon/dreamshaper-8(default)
• cyberdelia/CyberRealistic_V5
• dreamlike-art/dreamlike-photoreal-2.0
• fluently/Fluently-v4
• s6yx/ReV_Animated
• SG161222/Realistic_Vision_V5
• stable-diffusion-v1-5/stable-diffusion-v1-5
• XpucT/Deliberate_v6

Styles

Styles are prompt templates that wrap your positive and negative prompts. Inspired by twri/sdxl_prompt_styler.

💡 When using syles, start with a simple prompt like portrait of a cat or landscape of a mountain range.

Scale

Rescale up to 4x using Real-ESRGAN with weights from ai-forever. Necessary for high-resolution images.

Image-to-Image

The Image-to-Image settings allows you to provide input images for the initial latent, ControlNet, and IP-Adapter.

Strength

Initial image strength (known as 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.

💡 Denoising strength only applies to the Initial Image input; it doesn't affect ControlNet or IP-Adapter.

ControlNet

In ControlNet, the input image is used to get a feature map from an annotator. These are computer vision models used for tasks like edge detection and pose estimation. ControlNet models are trained to understand these feature maps. Read the docs to learn more.

Currently, the only annotator available is Canny (edge detection).

IP-Adapter

In an image-to-image pipeline, the input image is used as the initial latent representation. With IP-Adapter, the image is processed by a separate image encoder and the encoded features are used as conditioning along with the text prompt.

For capturing faces, enable IP-Adapter Face to use the full-face model. You should use an input image that is mostly a face and it should be high quality. You can generate fake portraits with Realistic Vision to experiment.

Advanced

Textual Inversion

Enable Use negative TI to append fast_negative to your negative prompt. Read An Image is Worth One Word to learn more.

DeepCache

DeepCache caches lower UNet layers and reuses them every n steps. Trade quality for speed:

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