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## Params | |
TL;DR: Tweak **steps**, **cfg scale** and **sampler** as results will vary depending on combination of all three | |
- **Encoder** | |
Which text tokenizer to use, SD typically uses `CLiP`, but others can be substituted (`BERT`, `GPTx`, etc) | |
- **Batch Size** | |
How many images to generate in parallel, limited by your VRAM | |
- **Batch Count** | |
How many batches to run sequentially | |
So total number of images generated is batch size x batch count | |
- **Seed** | |
Initializer for noise generator | |
Use same seed to have repeatable results, otherwise use random (-1) | |
- **CFG Scale** (Classifer-Free-Guidance) | |
How close should diffusers follow prompt, 0 means none and 30 means exact | |
Best results are between 7 (creative) to 13 (realistic), but optimal value depends on your model, prompt, and parameters | |
- **Width & Height** | |
SD 1.x was trained on 512x512, SD 2.x on 768x768, SDXL on 1024x1024, derivative fine-tunes may have different resolutions | |
So typically don't stray too far from those and instead use upscalers if high resolution is needed | |
However, changing aspect ratio can change composition of image (e.g. portrait vs landscape results in close-up vs more wide angle results) | |
- **Steps** | |
Directly impacts performance | |
How many iterative denoising steps to run, low number can lead to non-converged results (denoising is not complete) | |
Sweet-spot depends on chosen sampler and settings, can be as low as 10 and as high as 100 | |
Higher number of steps tends to increase output quality, except for non-converging (ancestral) samplers like "Euler a" which just keep modifying the picture to no end | |
At high step counts, many samplers converge to the same image as other samplers | |
## Prompt Engineering | |
Know your model: different models were trained on different datasets, some may understand terms other models don't | |
**Main groups** | |
- **Mediums**: best starting a prompt with it after specifying artist | |
Examples: *painting, photograph, drawing, sketch* | |
- **Flavors**: best left as separate token at the end of the prompt | |
Examples: *ray tracing, fine art, black and white, pixiv, artstation* | |
- **Movements**: best added to prompt with as keyword | |
Examples: *pop art, photorealism* | |
- **Artists**: best starting a prompt with it | |
Examples: *greg rutkowski, artgerm, dc comics, picasso* | |
**Modifiers** | |
- **Feel**: best near the end | |
Examples: *beautiful, sharp focus, 4k, hdr, high detailed, canon 5d* | |
- **Composition**: best at front, but only use if results don't fit | |
Examples: *1man, 1woman* | |
**Negative Prompt** | |
- Any keyword can be specified in a negative prompt as well | |
Examples: *watermark* | |
**Advanced Prompt Modifiers** | |
- Availability depends on implementation | |
- Specify importance of specific words: E.g. using `(word:1.2)` makes the influence of `word` stronger, `(word:0.8)` makes it weaker | |
**Advanced Prompt Modifiers** | |
For original backend only: | |
- Alternate between words: `[word1|word2]` will alternate between `word1` and `word2` in every denoising step, blending the two concepts | |
- Switch words during denoising: `[word1:word2:0.3]` will use `word1` for the first 30% of steps, then change it to `word2` | |
- Force include multiple objects "AND" | |
**Hints** | |
- Use either artists or movements | |
Using both may result in one overpowering the other, or in unexpected outcome | |
- Select medium that fits artist | |
It helps model a lot to know which medium to use when styling | |
- Add action after subject | |
Examples: portrait, standing, sitting | |
- Moving things to the front of prompt may increase its emphasis | |
Example: *cartoon drawing of a woman as pixar* vs *pixar drawing of a woman* | |
- Use both subject and scene keywords | |
Example: *woman on a beach* | |
**Example** | |
> (composition) (artist) (medium) (subject) (action) (scene) (movement) (flavor) (feel) | |
> 1woman greg rutkowski painting of a woman happy front portrait on a beach as photorealism, sharp focus, artstation | |