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https://github.com/invoke-ai/InvokeAI
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most (all?) references to CLI deprecated
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Lincoln Stein
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@ -71,6 +71,3 @@ under the selected name and register it with InvokeAI.
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use InvokeAI conventions - only alphanumeric letters and the
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characters ".+-".
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## Caveats
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This is a new script and may contain bugs.
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@ -31,10 +31,22 @@ turned on and off on the command line using `--nsfw_checker` and
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At installation time, InvokeAI will ask whether the checker should be
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activated by default (neither argument given on the command line). The
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response is stored in the InvokeAI initialization file (usually
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`invokeai.init` in your home directory). You can change the default at any
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time by opening this file in a text editor and commenting or
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uncommenting the line `--nsfw_checker`.
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response is stored in the InvokeAI initialization file
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(`invokeai.yaml` in the InvokeAI root directory). You can change the
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default at any time by opening this file in a text editor and
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changing the line `nsfw_checker:` from true to false or vice-versa:
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```
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...
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Features:
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esrgan: true
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internet_available: true
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log_tokenization: false
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nsfw_checker: true
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patchmatch: true
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restore: true
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```
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## Caveats
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@ -79,11 +91,3 @@ generates. However, it does write metadata into the PNG data area,
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including the prompt used to generate the image and relevant parameter
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settings. These fields can be examined using the `sd-metadata.py`
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script that comes with the InvokeAI package.
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Note that several other Stable Diffusion distributions offer
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wavelet-based "invisible" watermarking. We have experimented with the
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library used to generate these watermarks and have reached the
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conclusion that while the watermarking library may be adding
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watermarks to PNG images, the currently available version is unable to
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retrieve them successfully. If and when a functioning version of the
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library becomes available, we will offer this feature as well.
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@ -18,43 +18,16 @@ Output Example:
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## **Seamless Tiling**
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The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the
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`--seamless` option when starting the script which will result in all generated images to tile, or
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for each `invoke>` prompt as shown here:
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The seamless tiling mode causes generated images to seamlessly tile
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with itself creating repetitive wallpaper-like patterns. To use it,
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activate the Seamless Tiling option in the Web GUI and then select
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whether to tile on the X (horizontal) and/or Y (vertical) axes. Tiling
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will then be active for the next set of generations.
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A nice prompt to test seamless tiling with is:
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```python
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invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
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```
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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`.
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Possible values are `x`, `y`, and `x,y`:
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```python
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invoke> "pond garden with lotus by claude monet" --seamless --seamless_axes=x -s100 -n4
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```
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---
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## **Shortcuts: Reusing Seeds**
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Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version
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1.11. Provide a `-S` (or `--seed`) switch of `-1` to use the seed of the most recent image
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generated. If you produced multiple images with the `-n` switch, then you can go back further
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using `-2`, `-3`, etc. up to the first image generated by the previous command. Sorry, but you can't go
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back further than one command.
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Here's an example of using this to do a quick refinement. It also illustrates using the new `-G`
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switch to turn on upscaling and face enhancement (see previous section):
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```bash
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invoke> a cute child playing hopscotch -G0.5
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[...]
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outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
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# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
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invoke> a cute child playing hopscotch -G1.0 -s100 -S -1
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reusing previous seed 3498014304
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[...]
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outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
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pond garden with lotus by claude monet"
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```
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---
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@ -73,66 +46,27 @@ This will tell the sampler to invest 25% of its effort on the tabby cat aspect o
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on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
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combination of integers and floating point numbers, and they do not need to add up to 1.
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---
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## **Filename Format**
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The argument `--fnformat` allows to specify the filename of the
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image. Supported wildcards are all arguments what can be set such as
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`perlin`, `seed`, `threshold`, `height`, `width`, `gfpgan_strength`,
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`sampler_name`, `steps`, `model`, `upscale`, `prompt`, `cfg_scale`,
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`prefix`.
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The following prompt
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```bash
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dream> a red car --steps 25 -C 9.8 --perlin 0.1 --fnformat {prompt}_steps.{steps}_cfg.{cfg_scale}_perlin.{perlin}.png
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```
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generates a file with the name: `outputs/img-samples/a red car_steps.25_cfg.9.8_perlin.0.1.png`
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---
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## **Thresholding and Perlin Noise Initialization Options**
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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.
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Under the Noise section of the Web UI, you will find two options named
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Perlin Noise and Noise Threshold. [Perlin
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noise](https://en.wikipedia.org/wiki/Perlin_noise) is a type of
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structured noise used to simulate terrain and other natural
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textures. The slider controls the percentage of perlin noise that will
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be mixed into the image at the beginning of generation. Adding a little
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perlin noise to a generation will alter the image substantially.
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The noise threshold limits the range of the latent values during
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sampling and helps combat the oversharpening seem with higher CFG
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scale values.
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For better intuition into what these options do in practice:
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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
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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):
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```bash
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invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
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```
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Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2:
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```bash
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invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
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```
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!!! note
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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.
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---
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## **Simplified API**
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For programmers who wish to incorporate stable-diffusion into other products, this repository
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includes a simplified API for text to image generation, which lets you create images from a prompt
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in just three lines of code:
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```bash
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from ldm.generate import Generate
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g = Generate()
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outputs = g.txt2img("a unicorn in manhattan")
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```
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Outputs is a list of lists in the format [filename1,seed1],[filename2,seed2]...].
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Please see the documentation in ldm/generate.py for more information.
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---
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In generating this graphic, perlin noise at initialization was
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programmatically varied going across on the diagram by values 0.0,
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0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied
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going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are
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fixed using the prompt "a portrait of a beautiful young lady" a CFG of
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20, 100 steps, and a seed of 1950357039.
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@ -46,11 +46,19 @@ start the front end by selecting choice (3):
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```sh
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Do you want to generate images using the
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1. command-line
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2. browser-based UI
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3. textual inversion training
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4. open the developer console
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Please enter 1, 2, 3, or 4: [1] 3
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1: Browser-based UI
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2: Command-line interface
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3: Run textual inversion training
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4: Merge models (diffusers type only)
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5: Download and install models
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6: Change InvokeAI startup options
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7: Re-run the configure script to fix a broken install
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8: Open the developer console
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9: Update InvokeAI
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10: Command-line help
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Q: Quit
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Please enter 1-10, Q: [1]
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```
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From the command line, with the InvokeAI virtual environment active,
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@ -6,9 +6,7 @@ title: Variations
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## Intro
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Release 1.13 of SD-Dream adds support for image variations.
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You are able to do the following:
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InvokeAI's support for variations enables you to do the following:
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1. Generate a series of systematic variations of an image, given a prompt. The
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amount of variation from one image to the next can be controlled.
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@ -30,19 +28,7 @@ The prompt we will use throughout is:
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This will be indicated as `#!bash "prompt"` in the examples below.
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First we let SD create a series of images in the usual way, in this case
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requesting six iterations:
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```bash
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invoke> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
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...
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Outputs:
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./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
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./outputs/Xena/000001.1880768722.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1880768722
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./outputs/Xena/000001.332057179.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S332057179
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./outputs/Xena/000001.2224800325.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S2224800325
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./outputs/Xena/000001.465250761.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S465250761
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./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
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```
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requesting six iterations.
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<figure markdown>
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@ -53,22 +39,16 @@ Outputs:
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## Step 2 - Generating Variations
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Let's try to generate some variations. Using the same seed, we pass the argument
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`-v0.1` (or --variant_amount), which generates a series of variations each
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differing by a variation amount of 0.2. This number ranges from `0` to `1.0`,
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with higher numbers being larger amounts of variation.
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Let's try to generate some variations on this image. We select the "*"
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symbol in the line of icons above the image in order to fix the prompt
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and seed. Then we open up the "Variations" section of the generation
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panel and use the slider to set the variation amount to 0.2. The
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higher this value, the more each generated image will differ from the
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previous one.
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```bash
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invoke> "prompt" -n6 -S3357757885 -v0.2
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...
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Outputs:
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./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624:0.2 -S3357757885
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./outputs/Xena/000002.3647897225.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.2 -S3357757885
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./outputs/Xena/000002.917731034.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 917731034:0.2 -S3357757885
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./outputs/Xena/000002.4116285959.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 4116285959:0.2 -S3357757885
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./outputs/Xena/000002.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1614299449:0.2 -S3357757885
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./outputs/Xena/000002.1335553075.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1335553075:0.2 -S3357757885
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```
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Now we run the prompt a second time, requesting six iterations. You
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will see six images that are thematically related to each other. Try
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increasing and decreasing the variation amount and see what happens.
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### **Variation Sub Seeding**
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@ -773,7 +773,7 @@ class ModelManager(object):
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"""
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model_path: Path = None
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thing = path_url_or_repo # to save typing
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thing = str(path_url_or_repo) # to save typing
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self.logger.info(f"Probing {thing} for import")
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