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## :octicons-log-16: Important Changes Since Version 2.3
### Nodes
Behind the scenes, InvokeAI has been completely rewritten to support
"nodes," small unitary operations that can be combined into graphs to
form arbitrary workflows. For example, there is a prompt node that
processes the prompt string and feeds it to a text2latent node that
generates a latent image. The latents are then fed to a latent2image
node that translates the latent image into a PNG.
The WebGUI has a node editor that allows you to graphically design and
execute custom node graphs. The ability to save and load graphs is
still a work in progress, but coming soon.
### Command-Line Interface Retired
All "invokeai" command-line interfaces have been retired as of version
3.4.
To launch the Web GUI from the command-line, use the command
`invokeai-web` rather than the traditional `invokeai --web`.
### ControlNet
This version of InvokeAI features ControlNet, a system that allows you
to achieve exact poses for human and animal figures by providing a
model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
### New Schedulers
The list of schedulers has been completely revamped and brought up to date:
| **Short Name** | **Scheduler** | **Notes** |
|----------------|---------------------------------|-----------------------------|
| **ddim** | DDIMScheduler | |
| **ddpm** | DDPMScheduler | |
| **deis** | DEISMultistepScheduler | |
| **lms** | LMSDiscreteScheduler | |
| **pndm** | PNDMScheduler | |
| **heun** | HeunDiscreteScheduler | original noise schedule |
| **heun_k** | HeunDiscreteScheduler | using karras noise schedule |
| **euler** | EulerDiscreteScheduler | original noise schedule |
| **euler_k** | EulerDiscreteScheduler | using karras noise schedule |
| **kdpm_2** | KDPM2DiscreteScheduler | |
| **kdpm_2_a** | KDPM2AncestralDiscreteScheduler | |
| **dpmpp_2s** | DPMSolverSinglestepScheduler | |
| **dpmpp_2m** | DPMSolverMultistepScheduler | original noise scnedule |
| **dpmpp_2m_k** | DPMSolverMultistepScheduler | using karras noise schedule |
| **unipc** | UniPCMultistepScheduler | CPU only |
| **lcm** | LCMScheduler | |
Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.

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---
title: Command-Line Interface
---
# :material-bash: CLI
## **Interactive Command Line Interface**
The InvokeAI command line interface (CLI) provides scriptable access
to InvokeAI's features.Some advanced features are only available
through the CLI, though they eventually find their way into the WebUI.
The CLI is accessible from the `invoke.sh`/`invoke.bat` launcher by
selecting option (1). Alternatively, it can be launched directly from
the command line by activating the InvokeAI environment and giving the
command:
```bash
invokeai
```
After some startup messages, you will be presented with the `invoke> `
prompt. Here you can type prompts to generate images and issue other
commands to load and manipulate generative models. The CLI has a large
number of command-line options that control its behavior. To get a
concise summary of the options, call `invokeai` with the `--help` argument:
```bash
invokeai --help
```
The script uses the readline library to allow for in-line editing, command
history (++up++ and ++down++), autocompletion, and more. To help keep track of
which prompts generated which images, the script writes a log file of image
names and prompts to the selected output directory.
Here is a typical session
```bash
PS1:C:\Users\fred> invokeai
* Initializing, be patient...
* Initializing, be patient...
>> Initialization file /home/lstein/invokeai/invokeai.init found. Loading...
>> Internet connectivity is True
>> InvokeAI, version 2.3.0-rc5
>> InvokeAI runtime directory is "/home/lstein/invokeai"
>> GFPGAN Initialized
>> CodeFormer Initialized
>> ESRGAN Initialized
>> Using device_type cuda
>> xformers memory-efficient attention is available and enabled
(...more initialization messages...)
* Initialization done! Awaiting your command (-h for help, 'q' to quit)
invoke> ashley judd riding a camel -n2 -s150
Outputs:
outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
outputs/img-samples/00010.png: "ashley judd riding a camel" -n2 -s150 -S 1362479620
invoke> "there's a fly in my soup" -n6 -g
outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
invoke> q
```
![invoke-py-demo](../assets/dream-py-demo.png)
## Arguments
The script recognizes a series of command-line switches that will
change important global defaults, such as the directory for image
outputs and the location of the model weight files.
### List of arguments recognized at the command line
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
| Argument <img width="240" align="right"/> | Shortcut <img width="100" align="right"/> | Default <img width="320" align="right"/> | Description |
| ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
| `--help` | `-h` | | Print a concise help message. |
| `--outdir <path>` | `-o<path>` | `outputs/img_samples` | Location for generated images. |
| `--prompt_as_dir` | `-p` | `False` | Name output directories using the prompt text. |
| `--from_file <path>` | | `None` | Read list of prompts from a file. Use `-` to read from standard input |
| `--model <modelname>` | | `stable-diffusion-1.5` | Loads the initial model specified in configs/models.yaml. |
| `--ckpt_convert ` | | `False` | If provided both .ckpt and .safetensors files will be auto-converted into diffusers format in memory |
| `--autoconvert <path>` | | `None` | On startup, scan the indicated directory for new .ckpt/.safetensor files and automatically convert and import them |
| `--precision` | | `fp16` | Provide `fp32` for full precision mode, `fp16` for half-precision. `fp32` needed for Macintoshes and some NVidia 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 |
| `--patchmatch`, `--no-patchmatch` | | `--patchmatch` | Load/Don't load the PatchMatch inpainting extension |
| `--xformers`, `--no-xformers` | | `--xformers` | Load/Don't load the Xformers memory-efficient attention module (CUDA only) |
| `--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. |
| `--config <path>` | | `configs/models.yaml` | Configuration file for models and their weights. |
| `--iterations <int>` | `-n<int>` | `1` | How many images to generate per prompt. |
| `--width <int>` | `-W<int>` | `512` | Width of generated image |
| `--height <int>` | `-H<int>` | `512` | Height of generated image | `--steps <int>` | `-s<int>` | `50` | How many steps of refinement to apply |
| `--strength <float>` | `-s<float>` | `0.75` | For img2img: 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. |
| `--fit` | `-F` | `False` | For img2img: scale the init image to fit into the specified -H and -W dimensions |
| `--grid` | `-g` | `False` | Save all image series as a grid rather than individually. |
| `--sampler <sampler>` | `-A<sampler>` | `k_lms` | Sampler to use. Use `-h` to get list of available samplers. |
| `--seamless` | | `False` | Create interesting effects by tiling elements of the image. |
| `--embedding_path <path>` | | `None` | Path to pre-trained embedding manager checkpoints, for custom models |
| `--gfpgan_model_path` | | `experiments/pretrained_models/GFPGANv1.4.pth` | Path to GFPGAN model file. |
| `--free_gpu_mem` | | `False` | Free GPU memory after sampling, to allow image decoding and saving in low VRAM conditions |
| `--precision` | | `auto` | Set model precision, default is selected by device. Options: auto, float32, float16, autocast |
!!! warning "These arguments are deprecated but still work"
<div align="center" markdown>
| Argument | Shortcut | Default | Description |
|--------------------|------------|---------------------|--------------|
| `--full_precision` | | `False` | Same as `--precision=fp32`|
| `--weights <path>` | | `None` | Path to weights file; use `--model stable-diffusion-1.4` instead |
| `--laion400m` | `-l` | `False` | Use older LAION400m weights; use `--model=laion400m` instead |
</div>
!!! tip
On Windows systems, you may run into
problems when passing the invoke script standard backslashed path
names because the Python interpreter treats "\" as an escape.
You can either double your slashes (ick): `C:\\path\\to\\my\\file`, or
use Linux/Mac style forward slashes (better): `C:/path/to/my/file`.
## The .invokeai initialization file
To start up invoke.py with your preferred settings, place your desired
startup options in a file in your home directory named `.invokeai` The
file should contain the startup options as you would type them on the
command line (`--steps=10 --grid`), one argument per line, or a
mixture of both using any of the accepted command switch formats:
!!! example "my unmodified initialization file"
```bash title="~/.invokeai" linenums="1"
# InvokeAI initialization file
# This is the InvokeAI initialization file, which contains command-line default values.
# Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
# or renaming it and then running invokeai-configure again.
# The --root option below points to the folder in which InvokeAI stores its models, configs and outputs.
--root="/Users/mauwii/invokeai"
# the --outdir option controls the default location of image files.
--outdir="/Users/mauwii/invokeai/outputs"
# You may place other frequently-used startup commands here, one or more per line.
# Examples:
# --web --host=0.0.0.0
# --steps=20
# -Ak_euler_a -C10.0
```
!!! note
The initialization file only accepts the command line arguments.
There are additional arguments that you can provide on the `invoke>` command
line (such as `-n` or `--iterations`) that cannot be entered into this file.
Also be alert for empty blank lines at the end of the file, which will cause
an arguments error at startup time.
## 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
([img2img](#img2img)), or to selectively alter chosen regions of the image
([inpainting](#inpainting)).
### txt2img
!!! example ""
```bash
invoke> waterfall and rainbow -W640 -H480
```
This will create the requested image with the dimensions 640 (width)
and 480 (height).
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](../features/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 |
| `--h_symmetry_time_pct <float>` | | `None` | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) |
| `--v_symmetry_time_pct <float>` | | `None` | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.) |
!!! note
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.
!!! example "This is a example of img2img"
```bash
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.
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. |
### inpainting
!!! example ""
```bash
invoke> waterfall and rainbow -I./vacation-photo.png -M./vacation-mask.png -W640 -H480 --fit
```
This will do the same thing as img2img, but image alterations will
only occur within transparent areas defined by the mask file specified
by `-M`. You may also supply just a single initial image with the areas
to overpaint made transparent, but you must be careful not to destroy
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:
| 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.
`--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:
```bash
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.
```bash
invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
```
### Custom Styles and Subjects
You can load and use hundreds of community-contributed Textual
Inversion models just by typing the appropriate trigger phrase. Please
see [Concepts Library](../features/CONCEPTS.md) for more details.
## Other Commands
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.
#### `!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.
Some examples:
!!! 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"
```bash
invoke> !fix 0000045.4829112.png -G0.8 -ft gfpgan
>> fixing outputs/img-samples/0000045.4829112.png
>> retrieved seed 4829112 and prompt "boy enjoying a banana split"
>> 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.
### 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. There are several
different model formats, each described in the [Model Installation
Guide](../installation/050_INSTALLING_MODELS.md).
#### `!models`
This prints out a list of the models defined in `config/models.yaml'. The active
model is bold-faced
Example:
<pre>
inpainting-1.5 not loaded Stable Diffusion inpainting model
<b>stable-diffusion-1.5 active Stable Diffusion v1.5</b>
waifu-diffusion not loaded Waifu Diffusion v1.4
</pre>
#### `!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.
#### `!import_model <hugging_face_repo_ID>`
This imports and installs a `diffusers`-style model that is stored on
the [HuggingFace Web Site](https://huggingface.co). You can look up
any [Stable Diffusion diffusers
model](https://huggingface.co/models?library=diffusers) and install it
with a command like the following:
```bash
!import_model prompthero/openjourney
```
#### `!import_model <path/to/diffusers/directory>`
If you have a copy of a `diffusers`-style model saved to disk, you can
import it by passing the path to model's top-level directory.
#### `!import_model <url>`
For a `.ckpt` or `.safetensors` file, if you have a direct download
URL for the file, you can provide it to `!import_model` and the file
will be downloaded and installed for you.
#### `!import_model <path/to/model/weights.ckpt>`
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).
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.
#### `!import_model <path/to/directory_of_models>`
If you provide the path of a directory that contains one or more
`.ckpt` or `.safetensors` files, the CLI will scan the directory and
interactively offer to import the models it finds there. Also see the
`--autoconvert` command-line option.
#### `!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.
Example:
<pre>
invoke> <b>!edit_model waifu-diffusion</b>
>> Editing model waifu-diffusion from configuration file ./configs/models.yaml
description: <b>Waifu diffusion v1.4beta</b>
weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b>
config: configs/stable-diffusion/v1-inference.yaml
width: 512
height: 512
>> New configuration:
waifu-diffusion:
config: configs/stable-diffusion/v1-inference.yaml
description: Waifu diffusion v1.4beta
weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
height: 512
width: 512
OK to import [n]? y
>> Caching model stable-diffusion-1.4 in system RAM
>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
...
</pre>
### History processing
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 `!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:
!!! example ""
```bash
invoke> !history
...
[14] happy woman sitting under tree wearing broad hat and flowing garment
[15] beautiful woman sitting under tree wearing broad hat and flowing garment
[18] beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6
[20] watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
[21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
...
invoke> !20
invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
```
####`!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.
!!! example "load the generation command for a single png file"
```bash
invoke> !fetch 0000015.8929913.png
# the script returns the next line, ready for editing and running:
invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5
```
!!! example "fetch the generation commands from a batch of files and store them into `selected.txt`"
```bash
invoke> !fetch outputs\selected-imgs\*.png selected.txt
```
#### `!replay`
This command replays a text file generated by !fetch or created manually
!!! example
```bash
invoke> !replay outputs\selected-imgs\selected.txt
```
!!! note
These commands may behave unexpectedly if given a PNG file that was
not generated by InvokeAI.
#### `!search <search string>`
This is similar to !history but it only returns lines that contain
`search string`. For example:
```bash
invoke> !search surreal
[21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194
```
#### `!clear`
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.
- 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++
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.
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:
```bash
invoke> zebra with a mustache -I./test-pictures<TAB>
-I./test-pictures/Lincoln-and-Parrot.png -I./test-pictures/zebra.jpg -I./test-pictures/madonna.png
-I./test-pictures/bad-sketch.png -I./test-pictures/man_with_eagle/
```
You can then type ++z++, hit ++tab++ again, and it will autofill to `zebra.jpg`.
More text completion features (such as autocompleting seeds) are on their way.

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---
title: Embiggen
---
# :material-loupe: Embiggen
**upscale your images on limited memory machines**
GFPGAN and Real-ESRGAN are both memory intensive. In order to avoid
crashes and memory overloads during the Stable Diffusion process,
these effects are applied after Stable Diffusion has completed its
work.
In single image generations, you will see the output right away but
when you are using multiple iterations, the images will first be
generated and then upscaled and face restored after that process is
complete. While the image generation is taking place, you will still
be able to preview the base images.
If you wish to stop during the image generation but want to upscale or
face restore a particular generated image, pass it again with the same
prompt and generated seed along with the `-U` and `-G` prompt
arguments to perform those actions.
## Embiggen
If you wanted to be able to do more (pixels) without running out of VRAM,
or you want to upscale with details that couldn't possibly appear
without the context of a prompt, this is the feature to try out.
Embiggen automates the process of taking an init image, upscaling it,
cutting it into smaller tiles that slightly overlap, running all the
tiles through img2img to refine details with respect to the prompt,
and "stitching" the tiles back together into a cohesive image.
It automatically computes how many tiles are needed, and so it can be fed
*ANY* size init image and perform Img2Img on it (though it will be run only
one tile at a time, which can cause problems, see the Note at the end).
If you're familiar with "GoBig" (ala [progrock-stable](https://github.com/lowfuel/progrock-stable))
it's similar to that, except it can work up to an arbitrarily large size
(instead of just 2x), with tile overlaps configurable as a ratio, and
has extra logic to re-run any number of the tile sub-sections of the image
if for example a small part of a huge run got messed up.
### Usage
`-embiggen <scaling_factor> <esrgan_strength> <overlap_ratio OR overlap_pixels>`
Takes a scaling factor relative to the size of the `--init_img` (`-I`), followed by
ESRGAN upscaling strength (0 - 1.0), followed by minimum amount of overlap
between tiles as a decimal ratio (0 - 1.0) *OR* a number of pixels.
The scaling factor is how much larger than the `--init_img` the output
should be, and will multiply both x and y axis, so an image that is a
scaling factor of 3.0 has 3*3= 9 times as many pixels, and will take
(at least) 9 times as long (see overlap for why it might be
longer). If the `--init_img` is already the right size `-embiggen 1`,
and it can also be less than one if the init_img is too big.
Esrgan_strength defaults to 0.75, and the overlap_ratio defaults to
0.25, both are optional.
Unlike Img2Img, the `--width` (`-W`) and `--height` (`-H`) arguments
do not control the size of the image as a whole, but the size of the
tiles used to Embiggen the image.
ESRGAN is used to upscale the `--init_img` prior to cutting it into
tiles/pieces to run through img2img and then stitch back
together. Embiggen can be run without ESRGAN; just set the strength to
zero (e.g. `-embiggen 1.75 0`). The output of Embiggen can also be
upscaled after it's finished (`-U`).
The overlap is the minimum that tiles will overlap with adjacent
tiles, specified as either a ratio or a number of pixels. How much the
tiles overlap determines the likelihood the tiling will be noticable,
really small overlaps (e.g. a couple of pixels) may produce noticeable
grid-like fuzzy distortions in the final stitched image. Though, as
the overlapping space doesn't contribute to making the image bigger,
and the larger the overlap the more tiles (and the more time) it will
take to finish.
Because the overlapping parts of tiles don't "contribute" to
increasing size, every tile after the first in a row or column
effectively only covers an extra `1 - overlap_ratio` on each axis. If
the input/`--init_img` is same size as a tile, the ideal (for time)
scaling factors with the default overlap (0.25) are 1.75, 2.5, 3.25,
4.0, etc.
`-embiggen_tiles <spaced list of tiles>`
An advanced usage useful if you only want to alter parts of the image
while running Embiggen. It takes a list of tiles by number to run and
replace onto the initial image e.g. `1 3 5`. It's useful for either
fixing problem spots from a previous Embiggen run, or selectively
altering the prompt for sections of an image - for creative or
coherency reasons.
Tiles are numbered starting with one, and left-to-right,
top-to-bottom. So, if you are generating a 3x3 tiled image, the
middle row would be `4 5 6`.
`-embiggen_strength <strength>`
Another advanced option if you want to experiment with the strength parameter
that embiggen uses when it calls Img2Img. Values range from 0.0 to 1.0
and lower values preserve more of the character of the initial image.
Values that are too high will result in a completely different end image,
while values that are too low will result in an image not dissimilar to one
you would get with ESRGAN upscaling alone. The default value is 0.4.
### Examples
!!! example ""
Running Embiggen with 512x512 tiles on an existing image, scaling up by a factor of 2.5x;
and doing the same again (default ESRGAN strength is 0.75, default overlap between tiles is 0.25):
```bash
invoke > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5
invoke > a photo of a forest at sunset -s 100 -W 512 -H 512 -I outputs/forest.png -f 0.4 -embiggen 2.5 0.75 0.25
```
If your starting image was also 512x512 this should have taken 9 tiles.
!!! example ""
If there weren't enough clouds in the sky of that forest you just made
(and that image is about 1280 pixels (512*2.5) wide A.K.A. three
512x512 tiles with 0.25 overlaps wide) we can replace that top row of
tiles:
```bash
invoke> a photo of puffy clouds over a forest at sunset -s 100 -W 512 -H 512 -I outputs/000002.seed.png -f 0.5 -embiggen_tiles 1 2 3
```
## Fixing Previously-Generated Images
It is easy to apply embiggen to any previously-generated file without having to
look up the original prompt and provide an initial image. Just use the
syntax `!fix path/to/file.png <embiggen>`. For example, you can rewrite the
previous command to look like this:
```bash
invoke> !fix ./outputs/000002.seed.png -embiggen_tiles 1 2 3
```
A new file named `000002.seed.fixed.png` will be created in the output directory. Note that
the `!fix` command does not replace the original file, unlike the behavior at generate time.
You do not need to provide the prompt, and `!fix` automatically selects a good strength for
embiggen-ing.
!!! note
Because the same prompt is used on all the tiled images, and the model
doesn't have the context of anything outside the tile being run - it
can end up creating repeated pattern (also called 'motifs') across all
the tiles based on that prompt. The best way to combat this is
lowering the `--strength` (`-f`) to stay more true to the init image,
and increasing the number of steps so there is more compute-time to
create the detail. Anecdotally `--strength` 0.35-0.45 works pretty
well on most things. It may also work great in some examples even with
the `--strength` set high for patterns, landscapes, or subjects that
are more abstract. Because this is (relatively) fast, you can also
preserve the best parts from each.
Author: [Travco](https://github.com/travco)

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---
title: Inpainting
---
# :octicons-paintbrush-16: 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.
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.
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
```
## **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 [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.
To see how this works in practice, here's an image of a still life painting that
I got off the web.
<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:
```bash
invoke> a baseball -I /path/to/still_life.png -tm orange
```
<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!
```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>`
It will generate three files:
- The image with the selected area highlighted.
- 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
- 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.
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
Outputs:
[941.1] outputs/img-samples/000019.curly.hair.deselected.png: !mask ./test-pictures/curly.png -tm hair 0.5
[941.2] outputs/img-samples/000019.curly.hair.selected.png: !mask ./test-pictures/curly.png -tm hair 0.5
[941.3] outputs/img-samples/000019.curly.hair.masked.png: !mask ./test-pictures/curly.png -tm hair 0.5
```
<figure markdown>
![curly](../assets/outpainting/curly.png)
<figcaption>Original image "curly.png"</figcaption>
</figure>
<figure markdown>
![curly hair selected](../assets/inpainting/000019.curly.hair.selected.png)
<figcaption>000019.curly.hair.selected.png</figcaption>
</figure>
<figure markdown>
![curly hair deselected](../assets/inpainting/000019.curly.hair.deselected.png)
<figcaption>000019.curly.hair.deselected.png</figcaption>
</figure>
<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:
```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
...
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
```
<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.
To install the inpainting model, follow the
[instructions](../installation/050_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:
```yml
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.
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.
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.
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.
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.
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
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.
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.
---
## Recipe for GIMP
[GIMP](https://www.gimp.org/) is a popular Linux photoediting tool.
1. Open image in GIMP.
2. Layer->Transparency->Add Alpha Channel
3. Use lasso tool to select region to mask
4. Choose Select -> Float to create a floating selection
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.
---
## Recipe for Adobe Photoshop
1. Open image in Photoshop
<figure markdown>
![step1](../assets/step1.png)
</figure>
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.
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.
<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++
<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!
<figure markdown>
![step7](../assets/step7.png)
</figure>
8. In the export dialogue, Make sure the "Save colour values from transparent
pixels" checkbox is selected.

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---
title: Outpainting
---
# :octicons-paintbrush-16: Outpainting
## Outpainting and outcropping
Outpainting is a process by which the AI generates parts of the image that are
outside its original frame. It can be used to fix up images in which the subject
is off center, or when some detail (often the top of someone's head!) is cut
off.
InvokeAI supports two versions of outpainting, one called "outpaint" and the
other "outcrop." They work slightly differently and each has its advantages and
drawbacks.
### Outpainting
Outpainting is the same as inpainting, except that the painting occurs in the
regions outside of the original image. To outpaint using the `invoke.py` command
line script, prepare an image in which the borders to be extended are pure
black. Add an alpha channel (if there isn't one already), and make the borders
completely transparent and the interior completely opaque. If you wish to modify
the interior as well, you may create transparent holes in the transparency
layer, which `img2img` will paint into as usual.
Pass the image as the argument to the `-I` switch as you would for regular
inpainting:
```bash
invoke> a stream by a river -I /path/to/transparent_img.png
```
You'll likely be delighted by the results.
### Tips
1. Do not try to expand the image too much at once. Generally it is best to
expand the margins in 64-pixel increments. 128 pixels often works, but your
mileage may vary depending on the nature of the image you are trying to
outpaint into.
2. There are a series of switches that can be used to adjust how the inpainting
algorithm operates. In particular, you can use these to minimize the seam
that sometimes appears between the original image and the extended part.
These switches are:
| switch | default | description |
| -------------------------- | ------- | ---------------------------------------------------------------------- |
| `--seam_size SEAM_SIZE ` | `0` | Size of the mask around the seam between original and outpainted image |
| `--seam_blur SEAM_BLUR` | `0` | The amount to blur the seam inwards |
| `--seam_strength STRENGTH` | `0.7` | The img2img strength to use when filling the seam |
| `--seam_steps SEAM_STEPS` | `10` | The number of steps to use to fill the seam. |
| `--tile_size TILE_SIZE` | `32` | The tile size to use for filling outpaint areas |
### Outcrop
The `outcrop` extension gives you a convenient `!fix` postprocessing command
that allows you to extend a previously-generated image in 64 pixel increments in
any direction. You can apply the module to any image previously-generated by
InvokeAI. Note that it works with arbitrary PNG photographs, but not currently
with JPG or other formats. Outcropping is particularly effective when combined
with the
[runwayML custom inpainting model](INPAINTING.md#using-the-runwayml-inpainting-model).
Consider this image:
<figure markdown>
![curly_woman](../assets/outpainting/curly.png)
</figure>
Pretty nice, but it's annoying that the top of her head is cut off. She's also a
bit off center. Let's fix that!
```bash
invoke> !fix images/curly.png --outcrop top 128 right 64 bottom 64
```
This is saying to apply the `outcrop` extension by extending the top of the
image by 128 pixels, and the right and bottom of the image by 64 pixels. You can
use any combination of top|left|right|bottom, and specify any number of pixels
to extend. You can also abbreviate `--outcrop` to `-c`.
The result looks like this:
<figure markdown>
![curly_woman_outcrop](../assets/outpainting/curly-outcrop-2.png)
</figure>
The new image is larger than the original (576x704) because 64 pixels were added
to the top and right sides. You will need enough VRAM to process an image of
this size.
#### Outcropping non-InvokeAI images
You can outcrop an arbitrary image that was not generated by InvokeAI,
but your results will vary. The `inpainting-1.5` model is highly
recommended, but if not feasible, then you may be able to improve the
output by conditioning the outcropping with a text prompt that
describes the scene using the `--new_prompt` argument:
```bash
invoke> !fix images/vacation.png --outcrop top 128 --new_prompt "family vacation"
```
You may also provide a different seed for outcropping to use by passing
`-S<seed>`. A negative seed will generate a new random seed.
A number of caveats:
1. Although you can specify any pixel values, they will be rounded up to the
nearest multiple of 64. Smaller values are better. Larger extensions are more
likely to generate artefacts. However, if you wish you can run the !fix
command repeatedly to cautiously expand the image.
2. The extension is stochastic, meaning that each time you run it you'll get a
slightly different result. You can run it repeatedly until you get an image
you like. Unfortunately `!fix` does not currently respect the `-n`
(`--iterations`) argument.
3. Your results will be _much_ better if you use the `inpaint-1.5` model
released by runwayML and installed by default by `invokeai-configure`.
This model was trained specifically to harmoniously fill in image gaps. The
standard model will work as well, but you may notice color discontinuities at
the border.
4. When using the `inpaint-1.5` model, you may notice subtle changes to the area
outside the masked region. This is because the model performs an
encoding/decoding on the image as a whole. This does not occur with the
standard model.
## Outpaint
The `outpaint` extension does the same thing, but with subtle differences.
Starting with the same image, here is how we would add an additional 64 pixels
to the top of the image:
```bash
invoke> !fix images/curly.png --out_direction top 64
```
(you can abbreviate `--out_direction` as `-D`.
The result is shown here:
<figure markdown>
![curly_woman_outpaint](../assets/outpainting/curly-outpaint.png)
</figure>
Although the effect is similar, there are significant differences from
outcropping:
- You can only specify one direction to extend at a time.
- The image is **not** resized. Instead, the image is shifted by the specified
number of pixels. If you look carefully, you'll see that less of the lady's
torso is visible in the image.
- Because the image dimensions remain the same, there's no rounding to multiples
of 64.
- Attempting to outpaint larger areas will frequently give rise to ugly ghosting
effects.
- For best results, try increasing the step number.
- If you don't specify a pixel value in `-D`, it will default to half of the
whole image, which is likely not what you want.
!!! tip
Neither `outpaint` nor `outcrop` are perfect, but we continue to tune
and improve them. If one doesn't work, try the other. You may also
wish to experiment with other `img2img` arguments, such as `-C`, `-f`
and `-s`.

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# Translation
InvokeAI uses [Weblate](https://weblate.org) for translation. Weblate is a FOSS project providing a scalable translation service. Weblate automates the tedious parts of managing translation of a growing project, and the service is generously provided at no cost to FOSS projects like InvokeAI.
## Contributing
If you'd like to contribute by adding or updating a translation, please visit our [Weblate project](https://hosted.weblate.org/engage/invokeai/). You'll need to sign in with your GitHub account (a number of other accounts are supported, including Google).
Once signed in, select a language and then the Web UI component. From here you can Browse and Translate strings from English to your chosen language. Zen mode offers a simpler translation experience.
Your changes will be attributed to you in the automated PR process; you don't need to do anything else.
## Help & Questions
Please check Weblate's [documentation](https://docs.weblate.org/en/latest/index.html) or ping @psychedelicious or @blessedcoolant on Discord if you have any questions.
## Thanks
Thanks to the InvokeAI community for their efforts to translate the project!

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---
title: Variations
---
# :material-tune-variant: Variations
## Intro
InvokeAI's support for variations enables you to do the following:
1. Generate a series of systematic variations of an image, given a prompt. The
amount of variation from one image to the next can be controlled.
2. Given two or more variations that you like, you can combine them in a
weighted fashion.
!!! Information ""
This cheat sheet provides a quick guide for how this works in practice, using
variations to create the desired image of Xena, Warrior Princess.
## Step 1 -- Find a base image that you like
The prompt we will use throughout is:
`#!bash "lucy lawless as xena, warrior princess, character portrait, high resolution."`
This will be indicated as `#!bash "prompt"` in the examples below.
First we let SD create a series of images in the usual way, in this case
requesting six iterations.
<figure markdown>
![var1](../assets/variation_walkthru/000001.3357757885.png)
<figcaption> Seed 3357757885 looks nice </figcaption>
</figure>
---
## Step 2 - Generating Variations
Let's try to generate some variations on this image. We select the "*"
symbol in the line of icons above the image in order to fix the prompt
and seed. Then we open up the "Variations" section of the generation
panel and use the slider to set the variation amount to 0.2. The
higher this value, the more each generated image will differ from the
previous one.
Now we run the prompt a second time, requesting six iterations. You
will see six images that are thematically related to each other. Try
increasing and decreasing the variation amount and see what happens.
### **Variation Sub Seeding**
Note that the output for each image has a `-V` option giving the "variant
subseed" for that image, consisting of a seed followed by the variation amount
used to generate it.
This gives us a series of closely-related variations, including the two shown
here.
<figure markdown>
![var2](../assets/variation_walkthru/000002.3647897225.png)
<figcaption>subseed 3647897225</figcaption>
</figure>
<figure markdown>
![var3](../assets/variation_walkthru/000002.1614299449.png)
<figcaption>subseed 1614299449</figcaption>
</figure>
I like the expression on Xena's face in the first one (subseed 3647897225), and
the armor on her shoulder in the second one (subseed 1614299449). Can we combine
them to get the best of both worlds?
We combine the two variations using `-V` (`--with_variations`). Again, we must
provide the seed for the originally-chosen image in order for this to work.
```bash
invoke> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1
Outputs:
./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
```
Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The
resulting image is close, but not exactly what I wanted:
<figure markdown>
![var4](../assets/variation_walkthru/000003.1614299449.png)
<figcaption> subseed 1614299449 </figcaption>
</figure>
We could either try combining the images with different weights, or we can
generate more variations around the almost-but-not-quite image. We do the
latter, using both the `-V` (combining) and `-v` (variation strength) options.
Note that we use `-n6` to generate 6 variations:
```bash
invoke> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1 -v0.05 -n6
Outputs:
./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
```
This produces six images, all slight variations on the combination of the chosen
two images. Here's the one I like best:
<figure markdown>
![var5](../assets/variation_walkthru/000004.3747154981.png)
<figcaption> subseed 3747154981 </figcaption>
</figure>
As you can see, this is a very powerful tool, which when combined with subprompt
weighting, gives you great control over the content and quality of your
generated images.
## Variations and Samplers
The sampler you choose has a strong effect on variation strength. Some
samplers, such as `k_euler_a` are very "creative" and produce significant
amounts of image-to-image variation even when the seed is fixed and the
`-v` argument is very low. Others are more deterministic. Feel free to
experiment until you find the combination that you like.
Also be aware of the [Perlin Noise](../features/OTHER.md#thresholding-and-perlin-noise-initialization-options)
feature, which provides another way of introducing variability into your
image generation requests.

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---
title: F.A.Q.
---
# :material-frequently-asked-questions: F.A.Q.
## **Frequently-Asked-Questions**
Here are a few common installation problems and their solutions. Often these are
caused by incomplete installations or crashes during the install process.
---
### During `conda env create`, conda hangs indefinitely
If it is because of the last PIP step (usually stuck in the Git Clone step, you
can check the detailed log by this method):
```bash
export PIP_LOG="/tmp/pip_log.txt"
touch ${PIP_LOG}
tail -f ${PIP_LOG} &
conda env create -f environment-mac.yaml --debug --verbose
killall tail
rm ${PIP_LOG}
```
**SOLUTION**
Conda sometimes gets stuck at the last PIP step, in which several git
repositories are cloned and built.
Enter the stable-diffusion directory and completely remove the `src` directory
and all its contents. The safest way to do this is to enter the stable-diffusion
directory and give the command `git clean -f`. If this still doesn't fix the
problem, try "conda clean -all" and then restart at the `conda env create` step.
To further understand the problem to checking the install lot using this method:
```bash
export PIP_LOG="/tmp/pip_log.txt"
touch ${PIP_LOG}
tail -f ${PIP_LOG} &
conda env create -f environment-mac.yaml --debug --verbose
killall tail
rm ${PIP_LOG}
```
---
### `invoke.py` crashes with the complaint that it can't find `ldm.simplet2i.py`
Or it complains that function is being passed incorrect parameters.
**SOLUTION**
Reinstall the stable diffusion modules. Enter the `stable-diffusion` directory
and give the command `pip install -e .`
---
### Missing modules
`invoke.py` dies, complaining of various missing modules, none of which starts
with `ldm`.
**SOLUTION**
From within the `InvokeAI` directory, run `conda env update` This is also
frequently the solution to complaints about an unknown function in a module.
---
### How can I try new features
There's a feature or bugfix in the Stable Diffusion GitHub that you want to try
out.
**SOLUTIONS**
#### **Main Branch**
If the fix/feature is on the `main` branch, enter the stable-diffusion directory
and do a `git pull`.
Usually this will be sufficient, but if you start to see errors about missing or
incorrect modules, use the command `pip install -e .` and/or `conda env update`
(These commands won't break anything.)
`pip install -e .` and/or `conda env update -f environment.yaml`
(These commands won't break anything.)
#### **Sub Branch**
If the feature/fix is on a branch (e.g. "_foo-bugfix_"), the recipe is similar,
but do a `git pull <name of branch>`.
#### **Not Committed**
If the feature/fix is in a pull request that has not yet been made part of the
main branch or a feature/bugfix branch, then from the page for the desired pull
request, look for the line at the top that reads "_xxxx wants to merge xx
commits into lstein:main from YYYYYY_". Copy the URL in YYYY. It should have the
format
`https://github.com/<name of contributor>/stable-diffusion/tree/<name of branch>`
Then **go to the directory above stable-diffusion** and rename the directory to
"_stable-diffusion.lstein_", "_stable-diffusion.old_", or anything else. You can
then git clone the branch that contains the pull request:
`git clone https://github.com/<name of contributor>/stable-diffusion/tree/<name of branch>`
You will need to go through the install procedure again, but it should be fast
because all the dependencies are already loaded.
---
### CUDA out of memory
Image generation crashed with CUDA out of memory error after successful
sampling.
**SOLUTION**
Try to run script with option `--free_gpu_mem` This will free memory before
image decoding step.

10
mkdocs.yml
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@ -180,16 +180,6 @@ nav:
- Download Queue: 'contributing/DOWNLOAD_QUEUE.md'
- Translation: 'contributing/contribution_guides/translation.md'
- Tutorials: 'contributing/contribution_guides/tutorials.md'
- Deprecated:
- InvokeAI 2.3: 'deprecated/2to3.md'
- Command Line Interface: 'deprecated/CLI.md'
- Changelog: 'CHANGELOG.md'
- Variations: 'deprecated/VARIATIONS.md'
- Translations: 'deprecated/TRANSLATION.md'
- Embiggen: 'deprecated/EMBIGGEN.md'
- Inpainting: 'deprecated/INPAINTING.md'
- Outpainting: 'deprecated/OUTPAINTING.md'
- Troubleshooting: 'help/deprecated/TROUBLESHOOT.md'
- Help:
- Getting Started: 'help/gettingStartedWithAI.md'
- FAQs: 'help/FAQ.md'