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@ -8,9 +8,9 @@ hide:
## **Interactive Command Line Interface**
The `invoke.py` script, located in `scripts/`, provides an interactive
interface to image generation similar to the "invoke mothership" bot that Stable
AI provided on its Discord server.
The `invoke.py` script, located in `scripts/`, provides an interactive interface
to image generation similar to the "invoke mothership" bot that Stable AI
provided on its Discord server.
Unlike the `txt2img.py` and `img2img.py` scripts provided in the original
[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) source
@ -60,9 +60,9 @@ invoke> q
![invoke-py-demo](../assets/dream-py-demo.png)
The `invoke>` prompt's arguments are pretty much identical to those used in the
Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you do).
A significant change is that creation of individual images is now the default
unless `--grid` (`-g`) is given. A full list is given in
Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you
do). A significant change is that creation of individual images is now the
default unless `--grid` (`-g`) is given. A full list is given in
[List of prompt arguments](#list-of-prompt-arguments).
## Arguments
@ -75,7 +75,8 @@ the location of the model weight files.
These command-line arguments can be passed to `invoke.py` when you first run it
from the Windows, Mac or Linux command line. Some set defaults that can be
overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt-arguments). Others
overridden on a per-prompt basis (see
[List of prompt arguments](#list-of-prompt-arguments). Others
| Argument <img width="240" align="right"/> | Shortcut <img width="100" align="right"/> | Default <img width="320" align="right"/> | Description |
| ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
@ -85,8 +86,8 @@ overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt
| `--from_file <path>` | | `None` | Read list of prompts from a file. Use `-` to read from standard input |
| `--model <modelname>` | | `stable-diffusion-1.4` | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
| `--full_precision` | `-F` | `False` | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards. |
| `--png_compression <0-9>` | `-z<0-9>` | 6 | Select level of compression for output files, from 0 (no compression) to 9 (max compression) |
| `--safety-checker` | | False | Activate safety checker for NSFW and other potentially disturbing imagery |
| `--png_compression <0-9>` | `-z<0-9>` | `6` | Select level of compression for output files, from 0 (no compression) to 9 (max compression) |
| `--safety-checker` | | `False` | Activate safety checker for NSFW and other potentially disturbing imagery |
| `--web` | | `False` | Start in web server mode |
| `--host <ip addr>` | | `localhost` | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any. |
| `--port <port>` | | `9090` | Which port web server should listen for requests on. |
@ -122,9 +123,9 @@ overridden on a per-prompt basis (see [List of prompt arguments](#list-of-prompt
## List of prompt arguments
After the invoke.py script initializes, it will present you with a
`invoke>` prompt. Here you can enter information to generate images
from text ([txt2img](#txt2img)), to embellish an existing image or sketch
After the invoke.py script initializes, it will present you with a `invoke>`
prompt. Here you can enter information to generate images from text
([txt2img](#txt2img)), to embellish an existing image or sketch
([img2img](#img2img)), or to selectively alter chosen regions of the image
([inpainting](#inpainting)).
@ -141,60 +142,59 @@ from text ([txt2img](#txt2img)), to embellish an existing image or sketch
Here are the invoke> command that apply to txt2img:
| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description |
|--------------------|------------|---------------------|--------------|
| "my prompt" | | | Text prompt to use. The quotation marks are optional. |
| --width <int> | -W<int> | 512 | Width of generated image |
| --height <int> | -H<int> | 512 | Height of generated image |
| --iterations <int> | -n<int> | 1 | How many images to generate from this prompt |
| --steps <int> | -s<int> | 50 | How many steps of refinement to apply |
| --cfg_scale <float>| -C<float> | 7.5 | How hard to try to match the prompt to the generated image; any number greater than 1.0 works, but the useful range is roughly 5.0 to 20.0 |
| --seed <int> | -S<int> | None | Set the random seed for the next series of images. This can be used to recreate an image generated previously.|
| --sampler <sampler>| -A<sampler>| k_lms | Sampler to use. Use -h to get list of available samplers. |
| --karras_max <int> | | 29 | When using k_* samplers, set the maximum number of steps before shifting from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts) This value is sticky. [29] |
| --hires_fix | | | Larger images often have duplication artefacts. This option suppresses duplicates by generating the image at low res, and then using img2img to increase the resolution |
| --png_compression <0-9> | -z<0-9> | 6 | Select level of compression for output files, from 0 (no compression) to 9 (max compression) |
| --grid | -g | False | Turn on grid mode to return a single image combining all the images generated by this prompt |
| --individual | -i | True | Turn off grid mode (deprecated; leave off --grid instead) |
| --outdir <path> | -o<path> | outputs/img_samples | Temporarily change the location of these images |
| --seamless | | False | Activate seamless tiling for interesting effects |
| --seamless_axes | | x,y | Specify which axes to use circular convolution on. |
| --log_tokenization | -t | False | Display a color-coded list of the parsed tokens derived from the prompt |
| --skip_normalization| -x | False | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) |
| --upscale <int> <float> | -U <int> <float> | -U 1 0.75| Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75. |
| --facetool_strength <float> | -G <float> | -G0 | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) |
| --facetool <name> | -ft <name> | -ft gfpgan | Select face restoration algorithm to use: gfpgan, codeformer |
| --codeformer_fidelity | -cf <float> | 0.75 | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality |
| --save_original | -save_orig| False | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
| --variation <float> |-v<float>| 0.0 | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with -S<seed> and -n<int> to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
| --with_variations <pattern> | | None | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
| --save_intermediates <n> | | None | Save the image from every nth step into an "intermediates" folder inside the output directory |
| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description |
| ----------------------------------------- | ----------------------------------------- | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| "my prompt" | | | Text prompt to use. The quotation marks are optional. |
| `--width <int>` | `-W<int>` | `512` | Width of generated image |
| `--height <int>` | `-H<int>` | `512` | Height of generated image |
| `--iterations <int>` | `-n<int>` | `1` | How many images to generate from this prompt |
| `--steps <int>` | `-s<int>` | `50` | How many steps of refinement to apply |
| `--cfg_scale <float>` | `-C<float>` | `7.5` | How hard to try to match the prompt to the generated image; any number greater than 1.0 works, but the useful range is roughly 5.0 to 20.0 |
| `--seed <int>` | `-S<int>` | `None` | Set the random seed for the next series of images. This can be used to recreate an image generated previously. |
| `--sampler <sampler>` | `-A<sampler>` | `k_lms` | Sampler to use. Use -h to get list of available samplers. |
| `--karras_max <int>` | | `29` | When using k\_\* samplers, set the maximum number of steps before shifting from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts) This value is sticky. [29] |
| `--hires_fix` | | | Larger images often have duplication artefacts. This option suppresses duplicates by generating the image at low res, and then using img2img to increase the resolution |
| `--png_compression <0-9>` | `-z<0-9>` | `6` | Select level of compression for output files, from 0 (no compression) to 9 (max compression) |
| `--grid` | `-g` | `False` | Turn on grid mode to return a single image combining all the images generated by this prompt |
| `--individual` | `-i` | `True` | Turn off grid mode (deprecated; leave off --grid instead) |
| `--outdir <path>` | `-o<path>` | `outputs/img_samples` | Temporarily change the location of these images |
| `--seamless` | | `False` | Activate seamless tiling for interesting effects |
| `--seamless_axes` | | `x,y` | Specify which axes to use circular convolution on. |
| `--log_tokenization` | `-t` | `False` | Display a color-coded list of the parsed tokens derived from the prompt |
| `--skip_normalization` | `-x` | `False` | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) |
| `--upscale <int> <float>` | `-U <int> <float>` | `-U 1 0.75` | Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75. |
| `--facetool_strength <float>` | `-G <float> ` | `-G0` | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) |
| `--facetool <name>` | `-ft <name>` | `-ft gfpgan` | Select face restoration algorithm to use: gfpgan, codeformer |
| `--codeformer_fidelity` | `-cf <float>` | `0.75` | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality |
| `--save_original` | `-save_orig` | `False` | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
| `--variation <float>` | `-v<float>` | `0.0` | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
| `--with_variations <pattern>` | | `None` | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. |
| `--save_intermediates <n>` | | `None` | Save the image from every nth step into an "intermediates" folder inside the output directory |
Note that the width and height of the image must be multiples of
64. You can provide different values, but they will be rounded down to
the nearest multiple of 64.
Note that the width and height of the image must be multiples of 64. You can
provide different values, but they will be rounded down to the nearest multiple
of 64.
### This is an example of img2img:
### This is an example of img2img:
~~~~
```
invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
~~~~
```
This will modify the indicated vacation photograph by making it more
like the prompt. Results will vary greatly depending on what is in the
image. We also ask to --fit the image into a box no bigger than
640x480. Otherwise the image size will be identical to the provided
photo and you may run out of memory if it is large.
This will modify the indicated vacation photograph by making it more like the
prompt. Results will vary greatly depending on what is in the image. We also ask
to --fit the image into a box no bigger than 640x480. Otherwise the image size
will be identical to the provided photo and you may run out of memory if it is
large.
In addition to the command-line options recognized by txt2img, img2img
accepts additional options:
In addition to the command-line options recognized by txt2img, img2img accepts
additional options:
| Argument <img width="160" align="right"/> | Shortcut | Default | Description |
|----------------------|-------------|-----------------|--------------|
| `--init_img <path>` | `-I<path>` | `None` | Path to the initialization image |
| `--fit` | `-F` | `False` | Scale the image to fit into the specified -H and -W dimensions |
| `--strength <float>` | `-s<float>` | `0.75` | How hard to try to match the prompt to the initial image. Ranges from 0.0-0.99, with higher values replacing the initial image completely.|
| Argument <img width="160" align="right"/> | Shortcut | Default | Description |
| ----------------------------------------- | ----------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `--init_img <path>` | `-I<path>` | `None` | Path to the initialization image |
| `--fit` | `-F` | `False` | Scale the image to fit into the specified -H and -W dimensions |
| `--strength <float>` | `-s<float>` | `0.75` | How hard to try to match the prompt to the initial image. Ranges from 0.0-0.99, with higher values replacing the initial image completely. |
### inpainting
@ -211,41 +211,39 @@ accepts additional options:
the pixels underneath when you create the transparent areas. See
[Inpainting](./INPAINTING.md) for details.
inpainting accepts all the arguments used for txt2img and img2img, as
well as the --mask (-M) and --text_mask (-tm) arguments:
inpainting accepts all the arguments used for txt2img and img2img, as well as
the --mask (-M) and --text_mask (-tm) arguments:
| Argument <img width="100" align="right"/> | Shortcut | Default | Description |
|--------------------|------------|---------------------|--------------|
| `--init_mask <path>` | `-M<path>` | `None` |Path to an image the same size as the initial_image, with areas for inpainting made transparent.|
| `--invert_mask ` | | False |If true, invert the mask so that transparent areas are opaque and vice versa.|
| `--text_mask <prompt> [<float>]` | `-tm <prompt> [<float>]` | <none> | Create a mask from a text prompt describing part of the image|
| Argument <img width="100" align="right"/> | Shortcut | Default | Description |
| ----------------------------------------- | ------------------------ | ------- | ------------------------------------------------------------------------------------------------ |
| `--init_mask <path>` | `-M<path>` | `None` | Path to an image the same size as the initial_image, with areas for inpainting made transparent. |
| `--invert_mask ` | | False | If true, invert the mask so that transparent areas are opaque and vice versa. |
| `--text_mask <prompt> [<float>]` | `-tm <prompt> [<float>]` | <none> | Create a mask from a text prompt describing part of the image |
The mask may either be an image with transparent areas, in which case
the inpainting will occur in the transparent areas only, or a black
and white image, in which case all black areas will be painted into.
The mask may either be an image with transparent areas, in which case the
inpainting will occur in the transparent areas only, or a black and white image,
in which case all black areas will be painted into.
`--text_mask` (short form `-tm`) is a way to generate a mask using a
text description of the part of the image to replace. For example, if
you have an image of a breakfast plate with a bagel, toast and
scrambled eggs, you can selectively mask the bagel and replace it with
a piece of cake this way:
`--text_mask` (short form `-tm`) is a way to generate a mask using a text
description of the part of the image to replace. For example, if you have an
image of a breakfast plate with a bagel, toast and scrambled eggs, you can
selectively mask the bagel and replace it with a piece of cake this way:
~~~
```
invoke> a piece of cake -I /path/to/breakfast.png -tm bagel
~~~
```
The algorithm uses <a
href="https://github.com/timojl/clipseg">clipseg</a> to classify
different regions of the image. The classifier puts out a confidence
score for each region it identifies. Generally regions that score
above 0.5 are reliable, but if you are getting too much or too little
masking you can adjust the threshold down (to get more mask), or up
(to get less). In this example, by passing `-tm` a higher value, we
are insisting on a more stringent classification.
href="https://github.com/timojl/clipseg">clipseg</a> to classify different
regions of the image. The classifier puts out a confidence score for each region
it identifies. Generally regions that score above 0.5 are reliable, but if you
are getting too much or too little masking you can adjust the threshold down (to
get more mask), or up (to get less). In this example, by passing `-tm` a higher
value, we are insisting on a more stringent classification.
~~~
```
invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
~~~
```
# Other Commands
@ -253,31 +251,26 @@ The CLI offers a number of commands that begin with "!".
## Postprocessing images
To postprocess a file using face restoration or upscaling, use the
`!fix` command.
To postprocess a file using face restoration or upscaling, use the `!fix`
command.
### `!fix`
This command runs a post-processor on a previously-generated image. It
takes a PNG filename or path and applies your choice of the `-U`, `-G`, or
`--embiggen` switches in order to fix faces or upscale. If you provide a
filename, the script will look for it in the current output
directory. Otherwise you can provide a full or partial path to the
desired file.
This command runs a post-processor on a previously-generated image. It takes a
PNG filename or path and applies your choice of the `-U`, `-G`, or `--embiggen`
switches in order to fix faces or upscale. If you provide a filename, the script
will look for it in the current output directory. Otherwise you can provide a
full or partial path to the desired file.
Some examples:
!!! example ""
Upscale to 4X its original size and fix faces using codeformer:
!!! example "Upscale to 4X its original size and fix faces using codeformer"
```bash
invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
```
!!! example ""
Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen:
!!! example "Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen"
```bash
invoke> !fix 0000045.4829112.png -G0.8 -ft gfpgan
@ -286,26 +279,27 @@ Some examples:
>> GFPGAN - Restoring Faces for image seed:4829112
Outputs:
[1] outputs/img-samples/000017.4829112.gfpgan-00.png: !fix "outputs/img-samples/0000045.4829112.png" -s 50 -S -W 512 -H 512 -C 7.5 -A k_lms -G 0.8
```
### !mask
This command takes an image, a text prompt, and uses the `clipseg`
algorithm to automatically generate a mask of the area that matches
the text prompt. It is useful for debugging the text masking process
prior to inpainting with the `--text_mask` argument. See
[INPAINTING.md] for details.
This command takes an image, a text prompt, and uses the `clipseg` algorithm to
automatically generate a mask of the area that matches the text prompt. It is
useful for debugging the text masking process prior to inpainting with the
`--text_mask` argument. See [INPAINTING.md] for details.
## Model selection and importation
The CLI allows you to add new models on the fly, as well as to switch
among them rapidly without leaving the script.
The CLI allows you to add new models on the fly, as well as to switch among them
rapidly without leaving the script.
### !models
This prints out a list of the models defined in `config/models.yaml'.
The active model is bold-faced
This prints out a list of the models defined in `config/models.yaml'. The active
model is bold-faced
Example:
<pre>
laion400m not loaded <no description>
<b>stable-diffusion-1.4 active Stable Diffusion v1.4</b>
@ -314,13 +308,12 @@ waifu-diffusion not loaded Waifu Diffusion v1.3
### !switch <model>
This quickly switches from one model to another without leaving the
CLI script. `invoke.py` uses a memory caching system; once a model
has been loaded, switching back and forth is quick. The following
example shows this in action. Note how the second column of the
`!models` table changes to `cached` after a model is first loaded,
and that the long initialization step is not needed when loading
a cached model.
This quickly switches from one model to another without leaving the CLI script.
`invoke.py` uses a memory caching system; once a model has been loaded,
switching back and forth is quick. The following example shows this in action.
Note how the second column of the `!models` table changes to `cached` after a
model is first loaded, and that the long initialization step is not needed when
loading a cached model.
<pre>
invoke> !models
@ -360,24 +353,22 @@ waifu-diffusion cached Waifu Diffusion v1.3
### !import_model <path/to/model/weights>
This command imports a new model weights file into InvokeAI, makes it
available for image generation within the script, and writes out the
configuration for the model into `config/models.yaml` for use in
subsequent sessions.
This command imports a new model weights file into InvokeAI, makes it available
for image generation within the script, and writes out the configuration for the
model into `config/models.yaml` for use in subsequent sessions.
Provide `!import_model` with the path to a weights file ending in
`.ckpt`. If you type a partial path and press tab, the CLI will
autocomplete. Although it will also autocomplete to `.vae` files,
these are not currenty supported (but will be soon).
Provide `!import_model` with the path to a weights file ending in `.ckpt`. If
you type a partial path and press tab, the CLI will autocomplete. Although it
will also autocomplete to `.vae` files, these are not currenty supported (but
will be soon).
When you hit return, the CLI will prompt you to fill in additional
information about the model, including the short name you wish to use
for it with the `!switch` command, a brief description of the model,
the default image width and height to use with this model, and the
model's configuration file. The latter three fields are automatically
filled with reasonable defaults. In the example below, the bold-faced
text shows what the user typed in with the exception of the width,
height and configuration file paths, which were filled in
When you hit return, the CLI will prompt you to fill in additional information
about the model, including the short name you wish to use for it with the
`!switch` command, a brief description of the model, the default image width and
height to use with this model, and the model's configuration file. The latter
three fields are automatically filled with reasonable defaults. In the example
below, the bold-faced text shows what the user typed in with the exception of
the width, height and configuration file paths, which were filled in
automatically.
Example:
@ -412,12 +403,13 @@ invoke>
###!edit_model <name_of_model>
The `!edit_model` command can be used to modify a model that is
already defined in `config/models.yaml`. Call it with the short
name of the model you wish to modify, and it will allow you to
modify the model's `description`, `weights` and other fields.
The `!edit_model` command can be used to modify a model that is already defined
in `config/models.yaml`. Call it with the short name of the model you wish to
modify, and it will allow you to modify the model's `description`, `weights` and
other fields.
Example:
<pre>
invoke> <b>!edit_model waifu-diffusion</b>
>> Editing model waifu-diffusion from configuration file ./configs/models.yaml
@ -440,28 +432,28 @@ OK to import [n]? y
>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
...
</pre>
=======
invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3
...lots of text...
Outputs:
[2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25
```
======= invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3 ...lots of
text... Outputs: [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix
"outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512
-H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25 ```
## History processing
The CLI provides a series of convenient commands for reviewing previous
actions, retrieving them, modifying them, and re-running them.
The CLI provides a series of convenient commands for reviewing previous actions,
retrieving them, modifying them, and re-running them.
### !history
The invoke script keeps track of all the commands you issue during a
session, allowing you to re-run them. On Mac and Linux systems, it
also writes the command-line history out to disk, giving you access to
the most recent 1000 commands issued.
The invoke script keeps track of all the commands you issue during a session,
allowing you to re-run them. On Mac and Linux systems, it also writes the
command-line history out to disk, giving you access to the most recent 1000
commands issued.
The `!history` command will return a numbered list of all the commands
issued during the session (Windows), or the most recent 1000 commands
(Mac|Linux). You can then repeat a command by using the command `!NNN`,
where "NNN" is the history line number. For example:
The `!history` command will return a numbered list of all the commands issued
during the session (Windows), or the most recent 1000 commands (Mac|Linux). You
can then repeat a command by using the command `!NNN`, where "NNN" is the
history line number. For example:
```bash
invoke> !history
@ -478,14 +470,13 @@ invoke> watercolor of beautiful woman sitting under tree wearing broad hat and f
### !fetch
This command retrieves the generation parameters from a previously
generated image and either loads them into the command line
(Linux|Mac), or prints them out in a comment for copy-and-paste
(Windows). You may provide either the name of a file in the current
output directory, or a full file path. Specify path to a folder with
image png files, and wildcard *.png to retrieve the dream command used
to generate the images, and save them to a file commands.txt for
further processing.
This command retrieves the generation parameters from a previously generated
image and either loads them into the command line (Linux|Mac), or prints them
out in a comment for copy-and-paste (Windows). You may provide either the name
of a file in the current output directory, or a full file path. Specify path to
a folder with image png files, and wildcard \*.png to retrieve the dream command
used to generate the images, and save them to a file commands.txt for further
processing.
This example loads the generation command for a single png file:
@ -495,8 +486,8 @@ invoke> !fetch 0000015.8929913.png
invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
```
This one fetches the generation commands from a batch of files and
stores them into `selected.txt`:
This one fetches the generation commands from a batch of files and stores them
into `selected.txt`:
```bash
invoke> !fetch outputs\selected-imgs\*.png selected.txt
@ -506,12 +497,12 @@ invoke> !fetch outputs\selected-imgs\*.png selected.txt
This command replays a text file generated by !fetch or created manually
~~~
```
invoke> !replay outputs\selected-imgs\selected.txt
~~~
```
Note that these commands may behave unexpectedly if given a PNG file that
was not generated by InvokeAI.
Note that these commands may behave unexpectedly if given a PNG file that was
not generated by InvokeAI.
### !search <search string>
@ -525,42 +516,47 @@ invoke> !search surreal
### `!clear`
This clears the search history from memory and disk. Be advised that
this operation is irreversible and does not issue any warnings!
This clears the search history from memory and disk. Be advised that this
operation is irreversible and does not issue any warnings!
## Command-line editing and completion
The command-line offers convenient history tracking, editing, and
command completion.
The command-line offers convenient history tracking, editing, and command
completion.
- To scroll through previous commands and potentially edit/reuse them, use the ++up++ and ++down++ keys.
- To edit the current command, use the ++left++ and ++right++ keys to position the cursor, and then ++backspace++, ++delete++ or insert characters.
- To move to the very beginning of the command, type ++ctrl+a++ (or ++command+a++ on the Mac)
- To scroll through previous commands and potentially edit/reuse them, use the
++up++ and ++down++ keys.
- To edit the current command, use the ++left++ and ++right++ keys to position
the cursor, and then ++backspace++, ++delete++ or insert characters.
- To move to the very beginning of the command, type ++ctrl+a++ (or
++command+a++ on the Mac)
- To move to the end of the command, type ++ctrl+e++.
- To cut a section of the command, position the cursor where you want to start cutting and type ++ctrl+k++
- To paste a cut section back in, position the cursor where you want to paste, and type ++ctrl+y++
- To cut a section of the command, position the cursor where you want to start
cutting and type ++ctrl+k++
- To paste a cut section back in, position the cursor where you want to paste,
and type ++ctrl+y++
Windows users can get similar, but more limited, functionality if they
launch `invoke.py` with the `winpty` program and have the `pyreadline3`
library installed:
Windows users can get similar, but more limited, functionality if they launch
`invoke.py` with the `winpty` program and have the `pyreadline3` library
installed:
```batch
> winpty python scripts\invoke.py
```
On the Mac and Linux platforms, when you exit invoke.py, the last 1000
lines of your command-line history will be saved. When you restart
`invoke.py`, you can access the saved history using the ++up++ key.
On the Mac and Linux platforms, when you exit invoke.py, the last 1000 lines of
your command-line history will be saved. When you restart `invoke.py`, you can
access the saved history using the ++up++ key.
In addition, limited command-line completion is installed. In various
contexts, you can start typing your command and press ++tab++. A list of
potential completions will be presented to you. You can then type a
little more, hit ++tab++ again, and eventually autocomplete what you want.
In addition, limited command-line completion is installed. In various contexts,
you can start typing your command and press ++tab++. A list of potential
completions will be presented to you. You can then type a little more, hit
++tab++ again, and eventually autocomplete what you want.
When specifying file paths using the one-letter shortcuts, the CLI
will attempt to complete pathnames for you. This is most handy for the
`-I` (init image) and `-M` (init mask) paths. To initiate completion, start
the path with a slash (`/`) or `./`. For example:
When specifying file paths using the one-letter shortcuts, the CLI will attempt
to complete pathnames for you. This is most handy for the `-I` (init image) and
`-M` (init mask) paths. To initiate completion, start the path with a slash
(`/`) or `./`. For example:
```bash
invoke> zebra with a mustache -I./test-pictures<TAB>

310
docs/features/INPAINTING.md
View File

@ -6,29 +6,27 @@ title: Inpainting
## **Creating Transparent Regions for Inpainting**
Inpainting is really cool. To do it, you start with an initial image
and use a photoeditor to make one or more regions transparent
(i.e. they have a "hole" in them). You then provide the path to this
image at the dream> command line using the `-I` switch. Stable
Diffusion will only paint within the transparent region.
Inpainting is really cool. To do it, you start with an initial image and use a
photoeditor to make one or more regions transparent (i.e. they have a "hole" in
them). You then provide the path to this image at the dream> command line using
the `-I` switch. Stable Diffusion will only paint within the transparent region.
There's a catch. In the current implementation, you have to prepare
the initial image correctly so that the underlying colors are
preserved under the transparent area. Many imaging editing
applications will by default erase the color information under the
transparent pixels and replace them with white or black, which will
lead to suboptimal inpainting. It often helps to apply incomplete
transparency, such as any value between 1 and 99%
There's a catch. In the current implementation, you have to prepare the initial
image correctly so that the underlying colors are preserved under the
transparent area. Many imaging editing applications will by default erase the
color information under the transparent pixels and replace them with white or
black, which will lead to suboptimal inpainting. It often helps to apply
incomplete transparency, such as any value between 1 and 99%
You also must take care to export the PNG file in such a way that the
color information is preserved. There is often an option in the export
dialog that lets you specify this.
You also must take care to export the PNG file in such a way that the color
information is preserved. There is often an option in the export dialog that
lets you specify this.
If your photoeditor is erasing the underlying color information,
`dream.py` will give you a big fat warning. If you can't find a way to
coax your photoeditor to retain color values under transparent areas,
then you can combine the `-I` and `-M` switches to provide both the
original unedited image and the masked (partially transparent) image:
If your photoeditor is erasing the underlying color information, `dream.py` will
give you a big fat warning. If you can't find a way to coax your photoeditor to
retain color values under transparent areas, then you can combine the `-I` and
`-M` switches to provide both the original unedited image and the masked
(partially transparent) image:
```bash
invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent.png
@ -36,47 +34,47 @@ invoke> "man with cat on shoulder" -I./images/man.png -M./images/man-transparent
## **Masking using Text**
You can also create a mask using a text prompt to select the part of
the image you want to alter, using the <a
href="https://github.com/timojl/clipseg">clipseg</a> algorithm. This
works on any image, not just ones generated by InvokeAI.
You can also create a mask using a text prompt to select the part of the image
you want to alter, using the [clipseg](https://github.com/timojl/clipseg)
algorithm. This works on any image, not just ones generated by InvokeAI.
The `--text_mask` (short form `-tm`) option takes two arguments. The
first argument is a text description of the part of the image you wish
to mask (paint over). If the text description contains a space, you must
surround it with quotation marks. The optional second argument is the
minimum threshold for the mask classifier's confidence score, described
in more detail below.
The `--text_mask` (short form `-tm`) option takes two arguments. The first
argument is a text description of the part of the image you wish to mask (paint
over). If the text description contains a space, you must surround it with
quotation marks. The optional second argument is the minimum threshold for the
mask classifier's confidence score, described in more detail below.
To see how this works in practice, here's an image of a still life
painting that I got off the web.
To see how this works in practice, here's an image of a still life painting that
I got off the web.
<img src="../assets/still-life-scaled.jpg">
<figure markdown>
![still life scaled](../assets/still-life-scaled.jpg)
</figure>
You can selectively mask out the
orange and replace it with a baseball in this way:
You can selectively mask out the orange and replace it with a baseball in this
way:
~~~
```bash
invoke> a baseball -I /path/to/still_life.png -tm orange
~~~
```
<img src="../assets/still-life-inpainted.png">
<figure markdown>
![](../assets/still-life-inpainted.png)
</figure>
The clipseg classifier produces a confidence score for each region it
identifies. Generally regions that score above 0.5 are reliable, but
if you are getting too much or too little masking you can adjust the
threshold down (to get more mask), or up (to get less). In this
example, by passing `-tm` a higher value, we are insisting on a tigher
mask. However, if you make it too high, the orange may not be picked
up at all!
identifies. Generally regions that score above 0.5 are reliable, but if you are
getting too much or too little masking you can adjust the threshold down (to get
more mask), or up (to get less). In this example, by passing `-tm` a higher
value, we are insisting on a tigher mask. However, if you make it too high, the
orange may not be picked up at all!
~~~
```bash
invoke> a baseball -I /path/to/breakfast.png -tm orange 0.6
~~~
```
The `!mask` command may be useful for debugging problems with the
text2mask feature. The syntax is `!mask /path/to/image.png -tm <text>
<threshold>`
The `!mask` command may be useful for debugging problems with the text2mask
feature. The syntax is `!mask /path/to/image.png -tm <text> <threshold>`
It will generate three files:
@ -84,19 +82,18 @@ It will generate three files:
- it will be named XXXXX.<imagename>.<prompt>.selected.png
- The image with the un-selected area highlighted.
- it will be named XXXXX.<imagename>.<prompt>.deselected.png
- The image with the selected area converted into a black and white
image according to the threshold level
- The image with the selected area converted into a black and white image
according to the threshold level
- it will be named XXXXX.<imagename>.<prompt>.masked.png
The `.masked.png` file can then be directly passed to the `invoke>`
prompt in the CLI via the `-M` argument. Do not attempt this with
the `selected.png` or `deselected.png` files, as they contain some
transparency throughout the image and will not produce the desired
results.
The `.masked.png` file can then be directly passed to the `invoke>` prompt in
the CLI via the `-M` argument. Do not attempt this with the `selected.png` or
`deselected.png` files, as they contain some transparency throughout the image
and will not produce the desired results.
Here is an example of how `!mask` works:
```
```bash
invoke> !mask ./test-pictures/curly.png -tm hair 0.5
>> generating masks from ./test-pictures/curly.png
>> Initializing clipseg model for text to mask inference
@ -106,23 +103,30 @@ Outputs:
[941.3] outputs/img-samples/000019.curly.hair.masked.png: !mask ./test-pictures/curly.png -tm hair 0.5
```
**Original image "curly.png"**
<img src="../assets/outpainting/curly.png">
<figure markdown>
![curly](../assets/outpainting/curly.png)
<figcaption>Original image "curly.png"</figcaption>
</figure>
**000019.curly.hair.selected.png**
<img src="../assets/inpainting/000019.curly.hair.selected.png">
<figure markdown>
![curly hair selected](../assets/inpainting/000019.curly.hair.selected.png)
<figcaption>000019.curly.hair.selected.png</figcaption>
</figure>
**000019.curly.hair.deselected.png**
<img src="../assets/inpainting/000019.curly.hair.deselected.png">
<figure markdown>
![curly hair deselected](../assets/inpainting/000019.curly.hair.deselected.png)
<figcaption>000019.curly.hair.deselected.png</figcaption>
</figure>
**000019.curly.hair.masked.png**
<img src="../assets/inpainting/000019.curly.hair.masked.png">
<figure markdown>
![curly hair masked](../assets/inpainting/000019.curly.hair.masked.png)
<figcaption>000019.curly.hair.masked.png</figcaption>
</figure>
It looks like we selected the hair pretty well at the 0.5 threshold
(which is the default, so we didn't actually have to specify it), so
let's have some fun:
It looks like we selected the hair pretty well at the 0.5 threshold (which is
the default, so we didn't actually have to specify it), so let's have some fun:
```
```bash
invoke> medusa with cobras -I ./test-pictures/curly.png -M 000019.curly.hair.masked.png -C20
>> loaded input image of size 512x512 from ./test-pictures/curly.png
...
@ -130,86 +134,83 @@ Outputs:
[946] outputs/img-samples/000024.801380492.png: "medusa with cobras" -s 50 -S 801380492 -W 512 -H 512 -C 20.0 -I ./test-pictures/curly.png -A k_lms -f 0.75
```
<img src="../assets/inpainting/000024.801380492.png">
<figure markdown>
![](../assets/inpainting/000024.801380492.png)
</figure>
You can also skip the `!mask` creation step and just select the masked
region directly:
```
```bash
invoke> medusa with cobras -I ./test-pictures/curly.png -tm hair -C20
```
## Using the RunwayML inpainting model
The [RunwayML Inpainting Model
v1.5](https://huggingface.co/runwayml/stable-diffusion-inpainting) is
a specialized version of [Stable Diffusion
v1.5](https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5)
that contains extra channels specifically designed to enhance
inpainting and outpainting. While it can do regular `txt2img` and
`img2img`, it really shines when filling in missing regions. It has an
almost uncanny ability to blend the new regions with existing ones in
a semantically coherent way.
The
[RunwayML Inpainting Model v1.5](https://huggingface.co/runwayml/stable-diffusion-inpainting)
is a specialized version of
[Stable Diffusion v1.5](https://huggingface.co/spaces/runwayml/stable-diffusion-v1-5)
that contains extra channels specifically designed to enhance inpainting and
outpainting. While it can do regular `txt2img` and `img2img`, it really shines
when filling in missing regions. It has an almost uncanny ability to blend the
new regions with existing ones in a semantically coherent way.
To install the inpainting model, follow the
[instructions](INSTALLING-MODELS.md) for installing a new model. You
may use either the CLI (`invoke.py` script) or directly edit the
`configs/models.yaml` configuration file to do this. The main thing to
watch out for is that the the model `config` option must be set up to
use `v1-inpainting-inference.yaml` rather than the `v1-inference.yaml`
file that is used by Stable Diffusion 1.4 and 1.5.
[instructions](../installation/INSTALLING_MODELS.md) for installing a new model.
You may use either the CLI (`invoke.py` script) or directly edit the
`configs/models.yaml` configuration file to do this. The main thing to watch out
for is that the the model `config` option must be set up to use
`v1-inpainting-inference.yaml` rather than the `v1-inference.yaml` file that is
used by Stable Diffusion 1.4 and 1.5.
After installation, your `models.yaml` should contain an entry that
looks like this one:
After installation, your `models.yaml` should contain an entry that looks like
this one:
inpainting-1.5:
weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
description: SD inpainting v1.5
config: configs/stable-diffusion/v1-inpainting-inference.yaml
vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
width: 512
height: 512
inpainting-1.5: weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
description: SD inpainting v1.5 config:
configs/stable-diffusion/v1-inpainting-inference.yaml vae:
models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512
height: 512
As shown in the example, you may include a VAE fine-tuning weights
file as well. This is strongly recommended.
As shown in the example, you may include a VAE fine-tuning weights file as well.
This is strongly recommended.
To use the custom inpainting model, launch `invoke.py` with the
argument `--model inpainting-1.5` or alternatively from within the
script use the `!switch inpainting-1.5` command to load and switch to
the inpainting model.
To use the custom inpainting model, launch `invoke.py` with the argument
`--model inpainting-1.5` or alternatively from within the script use the
`!switch inpainting-1.5` command to load and switch to the inpainting model.
You can now do inpainting and outpainting exactly as described above,
but there will (likely) be a noticeable improvement in
coherence. Txt2img and Img2img will work as well.
You can now do inpainting and outpainting exactly as described above, but there
will (likely) be a noticeable improvement in coherence. Txt2img and Img2img will
work as well.
There are a few caveats to be aware of:
1. The inpainting model is larger than the standard model, and will
use nearly 4 GB of GPU VRAM. This makes it unlikely to run on
a 4 GB graphics card.
1. The inpainting model is larger than the standard model, and will use nearly 4
GB of GPU VRAM. This makes it unlikely to run on a 4 GB graphics card.
2. When operating in Img2img mode, the inpainting model is much less
steerable than the standard model. It is great for making small
changes, such as changing the pattern of a fabric, or slightly
changing a subject's expression or hair, but the model will
resist making the dramatic alterations that the standard
model lets you do.
2. When operating in Img2img mode, the inpainting model is much less steerable
than the standard model. It is great for making small changes, such as
changing the pattern of a fabric, or slightly changing a subject's expression
or hair, but the model will resist making the dramatic alterations that the
standard model lets you do.
3. While the `--hires` option works fine with the inpainting model,
some special features, such as `--embiggen` are disabled.
3. While the `--hires` option works fine with the inpainting model, some special
features, such as `--embiggen` are disabled.
4. Prompt weighting (`banana++ sushi`) and merging work well with
the inpainting model, but prompt swapping (a ("fluffy cat").swap("smiling dog") eating a hotdog`)
will not have any effect due to the way the model is set up.
You may use text masking (with `-tm thing-to-mask`) as an
effective replacement.
4. Prompt weighting (`banana++ sushi`) and merging work well with the inpainting
model, but prompt swapping
(`a ("fluffy cat").swap("smiling dog") eating a hotdog`) will not have any
effect due to the way the model is set up. You may use text masking (with
`-tm thing-to-mask`) as an effective replacement.
5. The model tends to oversharpen image if you use high step or CFG
values. If you need to do large steps, use the standard model.
5. The model tends to oversharpen image if you use high step or CFG values. If
you need to do large steps, use the standard model.
6. The `--strength` (`-f`) option has no effect on the inpainting
model due to its fundamental differences with the standard
model. It will always take the full number of steps you specify.
6. The `--strength` (`-f`) option has no effect on the inpainting model due to
its fundamental differences with the standard model. It will always take the
full number of steps you specify.
## Troubleshooting
@ -217,23 +218,21 @@ Here are some troubleshooting tips for inpainting and outpainting.
## Inpainting is not changing the masked region enough!
One of the things to understand about how inpainting works is that it
is equivalent to running img2img on just the masked (transparent)
area. img2img builds on top of the existing image data, and therefore
will attempt to preserve colors, shapes and textures to the best of
its ability. Unfortunately this means that if you want to make a
dramatic change in the inpainted region, for example replacing a red
wall with a blue one, the algorithm will fight you.
One of the things to understand about how inpainting works is that it is
equivalent to running img2img on just the masked (transparent) area. img2img
builds on top of the existing image data, and therefore will attempt to preserve
colors, shapes and textures to the best of its ability. Unfortunately this means
that if you want to make a dramatic change in the inpainted region, for example
replacing a red wall with a blue one, the algorithm will fight you.
You have a couple of options. The first is to increase the values of
the requested steps (`-sXXX`), strength (`-f0.XX`), and/or
condition-free guidance (`-CXX.X`). If this is not working for you, a
more extreme step is to provide the `--inpaint_replace 0.X` (`-r0.X`)
option. This value ranges from 0.0 to 1.0. The higher it is the less
attention the algorithm will pay to the data underneath the masked
region. At high values this will enable you to replace colored regions
entirely, but beware that the masked region mayl not blend in with the
surrounding unmasked regions as well.
You have a couple of options. The first is to increase the values of the
requested steps (`-sXXX`), strength (`-f0.XX`), and/or condition-free guidance
(`-CXX.X`). If this is not working for you, a more extreme step is to provide
the `--inpaint_replace 0.X` (`-r0.X`) option. This value ranges from 0.0 to 1.0.
The higher it is the less attention the algorithm will pay to the data
underneath the masked region. At high values this will enable you to replace
colored regions entirely, but beware that the masked region mayl not blend in
with the surrounding unmasked regions as well.
---
@ -248,8 +247,8 @@ surrounding unmasked regions as well.
5. Open the Layers toolbar (^L) and select "Floating Selection"
6. Set opacity to a value between 0% and 99%
7. Export as PNG
8. In the export dialogue, Make sure the "Save colour values from
transparent pixels" checkbox is selected.
8. In the export dialogue, Make sure the "Save colour values from transparent
pixels" checkbox is selected.
---
@ -261,36 +260,47 @@ surrounding unmasked regions as well.
![step1](../assets/step1.png)
</figure>
2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area
you desire to inpaint.
<figure markdown>
![step2](../assets/step2.png)
</figure>
3. Because we'll be applying a mask over the area we want to preserve, you should now select the inverse by using the ++shift+ctrl+i++ shortcut, or right clicking and using the "Select Inverse" option.
3. Because we'll be applying a mask over the area we want to preserve, you
should now select the inverse by using the ++shift+ctrl+i++ shortcut, or
right clicking and using the "Select Inverse" option.
4. You'll now create a mask by selecting the image layer, and Masking the selection. Make sure that you don't delete any of the underlying image, or your inpainting results will be dramatically impacted.
4. You'll now create a mask by selecting the image layer, and Masking the
selection. Make sure that you don't delete any of the underlying image, or
your inpainting results will be dramatically impacted.
<figure markdown>
![step4](../assets/step4.png)
</figure>
5. Make sure to hide any background layers that are present. You should see the mask applied to your image layer, and the image on your canvas should display the checkered background.
5. Make sure to hide any background layers that are present. You should see the
mask applied to your image layer, and the image on your canvas should display
the checkered background.
<figure markdown>
![step5](../assets/step5.png)
</figure>
6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
6. Save the image as a transparent PNG by using `File`-->`Save a Copy` from the
menu bar, or by using the keyboard shortcut ++alt+ctrl+s++
<figure markdown>
![step6](../assets/step6.png)
</figure>
7. After following the inpainting instructions above (either through the CLI or the Web UI), marvel at your newfound ability to selectively invoke. Lookin' good!
7. After following the inpainting instructions above (either through the CLI or
the Web UI), marvel at your newfound ability to selectively invoke. Lookin'
good!
<figure markdown>
![step7](../assets/step7.png)
</figure>
8. In the export dialogue, Make sure the "Save colour values from transparent pixels" checkbox is selected.
8. In the export dialogue, Make sure the "Save colour values from transparent
pixels" checkbox is selected.

100
docs/installation/INSTALL_LINUX.md
View File

@ -6,13 +6,13 @@ title: Manual Installation, Linux
## Installation
1. You will need to install the following prerequisites if they are not already
available. Use your operating system's preferred installer.
1. You will need to install the following prerequisites if they are not already
available. Use your operating system's preferred installer.
- Python (version 3.8.5 recommended; higher may work)
- git
2. Install the Python Anaconda environment manager.
2. Install the Python Anaconda environment manager.
```bash
~$ wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
@ -20,27 +20,29 @@ title: Manual Installation, Linux
~$ ./Anaconda3-2022.05-Linux-x86_64.sh
```
After installing anaconda, you should log out of your system and log back in. If
the installation worked, your command prompt will be prefixed by the name of the
current anaconda environment - `(base)`.
After installing anaconda, you should log out of your system and log back
in. If the installation worked, your command prompt will be prefixed by the
name of the current anaconda environment - `(base)`.
3. Copy the InvokeAI source code from GitHub:
3. Copy the InvokeAI source code from GitHub:
```bash
(base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
```
This will create InvokeAI folder where you will follow the rest of the steps.
This will create InvokeAI folder where you will follow the rest of the
steps.
4. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
4. Enter the newly-created InvokeAI folder. From this step forward make sure
that you are working in the InvokeAI directory!
```bash
(base) ~$ cd InvokeAI
(base) ~/InvokeAI$
```
5. Use anaconda to copy necessary python packages, create a new python
environment named `invokeai` and activate the environment.
5. Use anaconda to copy necessary python packages, create a new python
environment named `invokeai` and activate the environment.
```bash
(base) rm -rf src # (this is a precaution in case there is already a src directory)
@ -49,53 +51,69 @@ title: Manual Installation, Linux
(invokeai) ~/InvokeAI$
```
After these steps, your command prompt will be prefixed by `(invokeai)` as shown
above.
After these steps, your command prompt will be prefixed by `(invokeai)` as
shown above.
6. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
6. Load the big stable diffusion weights files and a couple of smaller
machine-learning models:
```bash
(invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
```
!!! note
This script will lead you through the process of creating an account on Hugging Face,
accepting the terms and conditions of the Stable Diffusion model license, and
obtaining an access token for downloading. It will then download and install the
weights files for you.
Please see [../features/INSTALLING_MODELS.md] for a manual process for doing the
same thing.
This script will lead you through the process of creating an account on Hugging Face,
accepting the terms and conditions of the Stable Diffusion model license,
and obtaining an access token for downloading. It will then download and
install the weights files for you.
7. Start generating images!
Please look [here](INSTALLING_MODELS.md) for a manual process for doing
the same thing.
# Command-line interface
(invokeai) python scripts/invoke.py
7. Start generating images!
# or run the web interface on localhost:9090!
(invokeai) python scripts/invoke.py --web
!!! todo "Run InvokeAI!"
# or run the web interface on your machine's network interface!
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
!!! warning "IMPORTANT"
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
Make sure that the conda environment is activated, which should create
`(invokeai)` in front of your prompt!
9. Subsequently, to relaunch the script, be sure to run "conda
activate invokeai" (step 5, second command), enter the `InvokeAI`
directory, and then launch the invoke script (step 8). If you forget
to activate the 'invokeai' environment, the script will fail with
multiple `ModuleNotFound` errors.
=== "CLI"
```bash
python scripts/invoke.py
```
=== "local Webserver"
```bash
python scripts/invoke.py --web
```
=== "Public Webserver"
```bash
python scripts/invoke.py --web --host 0.0.0.0
```
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
8. Subsequently, to relaunch the script, be sure to run "conda activate
invokeai" (step 5, second command), enter the `InvokeAI` directory, and then
launch the invoke script (step 8). If you forget to activate the 'invokeai'
environment, the script will fail with multiple `ModuleNotFound` errors.
## Updating to newer versions of the script
This distribution is changing rapidly. If you used the `git clone`
method (step 5) to download the InvokeAI directory, then to update to
the latest and greatest version, launch the Anaconda window, enter
`InvokeAI` and type:
This distribution is changing rapidly. If you used the `git clone` method
(step 5) to download the InvokeAI directory, then to update to the latest and
greatest version, launch the Anaconda window, enter `InvokeAI` and type:
```bash
(invokeai) ~/InvokeAI$ git pull

156
docs/installation/INSTALL_MAC.md
View File

@ -4,17 +4,18 @@ title: Manual Installation, macOS
# :fontawesome-brands-apple: macOS
Invoke AI runs quite well on M1 Macs and we have a number of M1 users
in the community.
Invoke AI runs quite well on M1 Macs and we have a number of M1 users in the
community.
While the repo does run on Intel Macs, we only have a couple
reports. If you have an Intel Mac and run into issues, please create
an issue on Github and we will do our best to help.
While the repo does run on Intel Macs, we only have a couple reports. If you
have an Intel Mac and run into issues, please create an issue on Github and we
will do our best to help.
## Requirements
- macOS 12.3 Monterey or later
- About 10GB of storage (and 10GB of data if your internet connection has data caps)
- About 10GB of storage (and 10GB of data if your internet connection has data
caps)
- Any M1 Macs or an Intel Macs with 4GB+ of VRAM (ideally more)
## Installation
@ -42,15 +43,13 @@ an issue on Github and we will do our best to help.
brew install cmake protobuf rust
```
Then clone the InvokeAI repository:
```bash title="Clone the InvokeAI repository:
# Clone the Invoke AI repo
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI
```
Choose the appropriate architecture for your system and install miniconda:
```bash title="Clone the InvokeAI repository"
# Clone the Invoke AI repo
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI
```
Choose the appropriate architecture for your system and install miniconda:
=== "M1 arm64"
@ -80,7 +79,7 @@ an issue on Github and we will do our best to help.
!!! todo "Clone the Invoke AI repo"
```bash
```bash
git clone https://github.com/invoke-ai/InvokeAI.git
cd InvokeAI
```
@ -102,6 +101,7 @@ an issue on Github and we will do our best to help.
```bash
# Activate the environment (you need to do this every time you want to run SD)
conda activate invokeai
```
!!! info
@ -111,38 +111,54 @@ an issue on Github and we will do our best to help.
!!! todo "Download the model weight files"
The `preload_models.py` script downloads and installs the model weight
files for you. It will lead you through the process of getting a Hugging Face
account, accepting the Stable Diffusion model weight license agreement, and
creating a download token:
The `preload_models.py` script downloads and installs the model weight
files for you. It will lead you through the process of getting a Hugging Face
account, accepting the Stable Diffusion model weight license agreement, and
creating a download token:
```bash
# This will take some time, depending on the speed of your internet connection
# and will consume about 10GB of space
(invokeai) python scripts/preload_models.py
python scripts/preload_models.py --no-interactive
```
!! todo "Run InvokeAI!"
!!! todo "Run InvokeAI!"
# Command-line interface
(invokeai) python scripts/invoke.py
!!! warning "IMPORTANT"
# or run the web interface on localhost:9090!
(invokeai) python scripts/invoke.py --web
Make sure that the conda environment is activated, which should create
`(invokeai)` in front of your prompt!
# or run the web interface on your machine's network interface!
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
=== "CLI"
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
```bash
python scripts/invoke.py
```
=== "local Webserver"
```bash
python scripts/invoke.py --web
```
=== "Public Webserver"
```bash
python scripts/invoke.py --web --host 0.0.0.0
```
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
---
## Common problems
After you followed all the instructions and try to run invoke.py, you might
get several errors. Here's the errors I've seen and found solutions for.
After you followed all the instructions and try to run invoke.py, you might get
several errors. Here's the errors I've seen and found solutions for.
### Is it slow?
@ -158,13 +174,12 @@ python ./scripts/orig_scripts/txt2img.py \
### Doesn't work anymore?
PyTorch nightly includes support for MPS. Because of this, this setup
is inherently unstable. One morning I woke up and it no longer worked
no matter what I did until I switched to miniforge. However, I have
another Mac that works just fine with Anaconda. If you can't get it to
work, please search a little first because many of the errors will get
posted and solved. If you can't find a solution please [create an
issue](https://github.com/invoke-ai/InvokeAI/issues).
PyTorch nightly includes support for MPS. Because of this, this setup is
inherently unstable. One morning I woke up and it no longer worked no matter
what I did until I switched to miniforge. However, I have another Mac that works
just fine with Anaconda. If you can't get it to work, please search a little
first because many of the errors will get posted and solved. If you can't find a
solution please [create an issue](https://github.com/invoke-ai/InvokeAI/issues).
One debugging step is to update to the latest version of PyTorch nightly.
@ -201,9 +216,9 @@ conda update \
There are several causes of these errors:
1. Did you remember to `conda activate invokeai`? If your terminal prompt begins with
"(invokeai)" then you activated it. If it begins with "(base)" or something else
you haven't.
1. Did you remember to `conda activate invokeai`? If your terminal prompt begins
with "(invokeai)" then you activated it. If it begins with "(base)" or
something else you haven't.
2. You might've run `./scripts/preload_models.py` or `./scripts/invoke.py`
instead of `python ./scripts/preload_models.py` or
@ -214,21 +229,21 @@ There are several causes of these errors:
3. if it says you're missing taming you need to rebuild your virtual
environment.
```bash
conda deactivate
conda env remove -n invokeai
conda env create -f environment-mac.yml
```
```bash
conda deactivate
conda env remove -n invokeai
conda env create -f environment-mac.yml
```
4. If you have activated the invokeai virtual environment and tried rebuilding it,
maybe the problem could be that I have something installed that you don't and
you'll just need to manually install it. Make sure you activate the virtual
environment so it installs there instead of globally.
4. If you have activated the invokeai virtual environment and tried rebuilding
it, maybe the problem could be that I have something installed that you don't
and you'll just need to manually install it. Make sure you activate the
virtual environment so it installs there instead of globally.
```bash
conda activate invokeai
pip install <package name>
```
```bash
conda activate invokeai
pip install <package name>
```
You might also need to install Rust (I mention this again below).
@ -382,11 +397,11 @@ curl \
### How come `--seed` doesn't work?
First this:
!!! Information
> Completely reproducible results are not guaranteed across PyTorch releases,
> individual commits, or different platforms. Furthermore, results may not be
> reproducible between CPU and GPU executions, even when using identical seeds.
Completely reproducible results are not guaranteed across PyTorch releases,
individual commits, or different platforms. Furthermore, results may not be
reproducible between CPU and GPU executions, even when using identical seeds.
[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)
@ -447,11 +462,11 @@ C.
### I just got Rickrolled! Do I have a virus?
You don't have a virus. It's part of the project. Here's
[Rick](https://github.com/invoke-ai/InvokeAI/blob/main/assets/rick.jpeg)
and here's [the
code](https://github.com/invoke-ai/InvokeAI/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
that swaps him in. It's a NSFW filter, which IMO, doesn't work very
good (and we call this "computer vision", sheesh).
[Rick](https://github.com/invoke-ai/InvokeAI/blob/main/assets/rick.jpeg) and
here's
[the code](https://github.com/invoke-ai/InvokeAI/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
that swaps him in. It's a NSFW filter, which IMO, doesn't work very good (and we
call this "computer vision", sheesh).
---
@ -474,9 +489,9 @@ return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backen
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
```
Update to the latest version of invoke-ai/InvokeAI. We were
patching pytorch but we found a file in stable-diffusion that we could
change instead. This is a 32-bit vs 16-bit problem.
Update to the latest version of invoke-ai/InvokeAI. We were patching pytorch but
we found a file in stable-diffusion that we could change instead. This is a
32-bit vs 16-bit problem.
### The processor must support the Intel bla bla bla
@ -508,4 +523,3 @@ Abort trap: 6
/Users/[...]/opt/anaconda3/envs/invokeai/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
```

53
docs/installation/INSTALL_WINDOWS.md
View File

@ -72,34 +72,51 @@ in the wiki
7. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
```bash
(invokeai) ~/InvokeAI$ python scripts\preload_models.py
python scripts/preload_models.py
```
!!! note
This script will lead you through the process of creating an account on Hugging Face,
accepting the terms and conditions of the Stable Diffusion model license, and
obtaining an access token for downloading. It will then download and install the
weights files for you.
Please see [../features/INSTALLING_MODELS.md] for a manual process for doing the
same thing.
This script will lead you through the process of creating an account on Hugging Face,
accepting the terms and conditions of the Stable Diffusion model license, and
obtaining an access token for downloading. It will then download and install the
weights files for you.
Please look [here](INSTALLING_MODELS.md) for a manual process for doing the
same thing.
8. Start generating images!
# Command-line interface
(invokeai) python scripts/invoke.py
!!! example ""
# or run the web interface on localhost:9090!
(invokeai) python scripts/invoke.py --web
!!! warning "IMPORTANT"
# or run the web interface on your machine's network interface!
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
Make sure that the conda environment is activated, which should create
`(invokeai)` in front of your prompt!
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
=== "CLI"
```bash
python scripts/invoke.py
```
=== "local Webserver"
```bash
python scripts/invoke.py --web
```
=== "Public Webserver"
```bash
python scripts/invoke.py --web --host 0.0.0.0
```
To use an alternative model you may invoke the `!switch` command in
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
either the CLI or the Web UI. See [Command Line
Client](../features/CLI.md#model-selection-and-importation). The
model names are defined in `configs/models.yaml`.
9. Subsequently, to relaunch the script, first activate the Anaconda
command window (step 3),enter the InvokeAI directory (step 5, `cd