mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
162 lines
7.1 KiB
Markdown
162 lines
7.1 KiB
Markdown
---
|
|
title: Others
|
|
---
|
|
|
|
# :fontawesome-regular-share-from-square: Others
|
|
|
|
## **Google Colab**
|
|
|
|
[{ align="right" }](https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb)
|
|
|
|
Open and follow instructions to use an isolated environment running Dream.
|
|
|
|
Output Example:
|
|
|
|
|
|
|
|
---
|
|
|
|
## **Seamless Tiling**
|
|
|
|
The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the
|
|
`--seamless` option when starting the script which will result in all generated images to tile, or
|
|
for each `invoke>` prompt as shown here:
|
|
|
|
```python
|
|
invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
|
|
```
|
|
|
|
By default this will tile on both the X and Y axes. However, you can also specify specific axes to tile on with `--seamless_axes`.
|
|
Possible values are `x`, `y`, and `x,y`:
|
|
```python
|
|
invoke> "pond garden with lotus by claude monet" --seamless --seamless_axes=x -s100 -n4
|
|
```
|
|
|
|
---
|
|
|
|
## **Shortcuts: Reusing Seeds**
|
|
|
|
Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version
|
|
1.11. Provide a `-S` (or `--seed`) switch of `-1` to use the seed of the most recent image
|
|
generated. If you produced multiple images with the `-n` switch, then you can go back further
|
|
using `-2`, `-3`, etc. up to the first image generated by the previous command. Sorry, but you can't go
|
|
back further than one command.
|
|
|
|
Here's an example of using this to do a quick refinement. It also illustrates using the new `-G`
|
|
switch to turn on upscaling and face enhancement (see previous section):
|
|
|
|
```bash
|
|
invoke> a cute child playing hopscotch -G0.5
|
|
[...]
|
|
outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
|
|
|
|
# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
|
|
invoke> a cute child playing hopscotch -G1.0 -s100 -S -1
|
|
reusing previous seed 3498014304
|
|
[...]
|
|
outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
|
|
```
|
|
|
|
---
|
|
|
|
## **Weighted Prompts**
|
|
|
|
You may weight different sections of the prompt to tell the sampler to attach different levels of
|
|
priority to them, by adding `:<percent>` to the end of the section you wish to up- or downweight. For
|
|
example consider this prompt:
|
|
|
|
```bash
|
|
tabby cat:0.25 white duck:0.75 hybrid
|
|
```
|
|
|
|
This will tell the sampler to invest 25% of its effort on the tabby cat aspect of the image and 75%
|
|
on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
|
|
combination of integers and floating point numbers, and they do not need to add up to 1.
|
|
|
|
---
|
|
|
|
## **Filename Format**
|
|
|
|
The argument `--fnformat` allows to specify the filename of the
|
|
image. Supported wildcards are all arguments what can be set such as
|
|
`perlin`, `seed`, `threshold`, `height`, `width`, `gfpgan_strength`,
|
|
`sampler_name`, `steps`, `model`, `upscale`, `prompt`, `cfg_scale`,
|
|
`prefix`.
|
|
|
|
The following prompt
|
|
```bash
|
|
dream> a red car --steps 25 -C 9.8 --perlin 0.1 --fnformat {prompt}_steps.{steps}_cfg.{cfg_scale}_perlin.{perlin}.png
|
|
```
|
|
|
|
generates a file with the name: `outputs/img-samples/a red car_steps.25_cfg.9.8_perlin.0.1.png`
|
|
|
|
---
|
|
|
|
## **Thresholding and Perlin Noise Initialization Options**
|
|
|
|
Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images.
|
|
|
|
For better intuition into what these options do in practice:
|
|
|
|
|
|
|
|
In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from
|
|
0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are fixed, so the initial prompt is as follows (no thresholding or perlin noise):
|
|
|
|
```bash
|
|
invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
|
|
```
|
|
|
|
Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2:
|
|
|
|
```bash
|
|
invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
|
|
```
|
|
|
|
!!! note
|
|
|
|
currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization.
|
|
|
|
---
|
|
|
|
## **Simplified API**
|
|
|
|
For programmers who wish to incorporate stable-diffusion into other products, this repository
|
|
includes a simplified API for text to image generation, which lets you create images from a prompt
|
|
in just three lines of code:
|
|
|
|
```bash
|
|
from ldm.generate import Generate
|
|
g = Generate()
|
|
outputs = g.txt2img("a unicorn in manhattan")
|
|
```
|
|
|
|
Outputs is a list of lists in the format [filename1,seed1],[filename2,seed2]...].
|
|
|
|
Please see ldm/generate.py for more information. A set of example scripts is coming RSN.
|
|
|
|
---
|
|
|
|
## **Preload Models**
|
|
|
|
In situations where you have limited internet connectivity or are blocked behind a firewall, you can
|
|
use the preload script to preload the required files for Stable Diffusion to run.
|
|
|
|
The preload script `scripts/preload_models.py` needs to be run once at least while connected to the
|
|
internet. In the following runs, it will load up the cached versions of the required files from the
|
|
`.cache` directory of the system.
|
|
|
|
```bash
|
|
(invokeai) ~/stable-diffusion$ python3 ./scripts/preload_models.py
|
|
preloading bert tokenizer...
|
|
Downloading: 100%|██████████████████████████████████| 28.0/28.0 [00:00<00:00, 49.3kB/s]
|
|
Downloading: 100%|██████████████████████████████████| 226k/226k [00:00<00:00, 2.79MB/s]
|
|
Downloading: 100%|██████████████████████████████████| 455k/455k [00:00<00:00, 4.36MB/s]
|
|
Downloading: 100%|██████████████████████████████████| 570/570 [00:00<00:00, 477kB/s]
|
|
...success
|
|
preloading kornia requirements...
|
|
Downloading: "https://github.com/DagnyT/hardnet/raw/master/pretrained/train_liberty_with_aug/checkpoint_liberty_with_aug.pth" to /u/lstein/.cache/torch/hub/checkpoints/checkpoint_liberty_with_aug.pth
|
|
100%|███████████████████████████████████████████████| 5.10M/5.10M [00:00<00:00, 101MB/s]
|
|
...success
|
|
```
|