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Zero
Diffusion ZERO
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 -.
For groups of tokens, wrap them in parentheses and multiply by a float between 0 and 2. For example, a (birthday cake)1.3 on a table will increase the weight of both birthday and cake by 1.3x. This also means the entire scene will be more birthday-like, not just the cake. To counteract this, you can use - inside the parentheses on specific tokens, e.g., a (birthday-- cake)1.3, to reduce the birthday aspect.
This is the same syntax used in InvokeAI and it differs from AUTOMATIC1111:
| Compel | AUTOMATIC1111 |
|---|---|
blue++ |
((blue)) |
blue-- |
[[blue]] |
(blue)1.2 |
(blue:1.2) |
(blue)0.8 |
(blue:0.8) |
Arrays
Arrays allow you to generate multiple different images from a single prompt. For example, an adult [[blonde,brunette]] [[man,woman]] will expand into 4 different prompts. This implementation was inspired by Fooocus.
NB: Make sure to set
Imagesto the number of images you want to generate. Otherwise, only the first prompt will be used.
Models
Each model checkpoint has a different aesthetic:
- Comfy-Org/stable-diffusion-v1-5: base
- cyberdelia/CyberRealistic_V5: realistic
- Lykon/dreamshaper-8: general purpose (default)
- fluently/Fluently-v4: general purpose stylized
- Linaqruf/anything-v3-1: anime
- prompthero/openjourney-v4: Midjourney art style
- SG161222/Realistic_Vision_V5: realistic
- XpucT/Deliberate_v6: general purpose stylized
LoRA
Apply up to 2 LoRA (low-rank adaptation) adapters with adjustable strength:
- Perfection Style: attempts to improve aesthetics, use high strength
- Detailed Style: attempts to improve details, use low strength
NB: The trigger words are automatically appended to the positive prompt for you.
Embeddings
Select one or more textual inversion embeddings:
fast_negative: all-purpose (default, recommended)cyberrealistic_negative: realistic add-on (for CyberRealistic)unrealistic_dream: realistic add-on (for RealisticVision)
NB: The trigger token is automatically appended to the negative prompt for you.
Styles
Styles are prompt templates that wrap your positive and negative prompts. They were originally derived from the twri/sdxl_prompt_styler Comfy node, but have since been entirely rewritten.
Start by framing a simple subject like portrait of a young adult woman or landscape of a mountain range and experiment.
Anime
The Anime: * styles work the best with Dreamshaper. When using the anime-specific Anything model, you should use the Anime: Anything style with the following settings:
- Scheduler:
DEIS 2MorDPM++ 2M - Guidance:
10 - Steps:
50
You subject should be a few simple tokens like girl, brunette, blue eyes, armor, nebula, celestial. Experiment with Clip Skip and Karras. Finish with the Perfection Style LoRA on a moderate setting and upscale.
Scale
Rescale up to 4x using Real-ESRGAN with weights from ai-forever. Necessary for high-resolution images.
Image-to-Image
The 🖼️ Image tab enables the image-to-image and IP-Adapter pipelines. Either use the image input or select a generation from the gallery. 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. Only applies to the image-to-image input.
IP-Adapter
In an image-to-image pipeline, the input image is used as the initial latent. With IP-Adapter, the input 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. Note that you'll never get true identity preservation without an advanced pipeline like InstantID, which combines many techniques.
Advanced
DeepCache
DeepCache caches lower UNet layers and reuses them every Interval steps. Trade quality for speed:
1: no caching (default)2: more quality3: balanced4: more speed
FreeU
FreeU re-weights the contributions sourced from the UNet’s skip connections and backbone feature maps. Can sometimes improve image quality.
Clip Skip
When enabled, the last CLIP layer is skipped. Can sometimes improve image quality.
Tiny VAE
Enable madebyollin/taesd for near-instant latent decoding with a minor loss in detail. Useful for development.