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590 lines
40 KiB
Markdown
---
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title: Command-Line Interface
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---
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# :material-bash: CLI
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## **Interactive Command Line Interface**
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The InvokeAI command line interface (CLI) provides scriptable access
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to InvokeAI's features.Some advanced features are only available
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through the CLI, though they eventually find their way into the WebUI.
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The CLI is accessible from the `invoke.sh`/`invoke.bat` launcher by
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selecting option (1). Alternatively, it can be launched directly from
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the command line by activating the InvokeAI environment and giving the
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command:
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```bash
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invokeai
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```
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After some startup messages, you will be presented with the `invoke> `
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prompt. Here you can type prompts to generate images and issue other
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commands to load and manipulate generative models. The CLI has a large
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number of command-line options that control its behavior. To get a
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concise summary of the options, call `invokeai` with the `--help` argument:
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```bash
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invokeai --help
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```
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The script uses the readline library to allow for in-line editing, command
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history (++up++ and ++down++), autocompletion, and more. To help keep track of
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which prompts generated which images, the script writes a log file of image
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names and prompts to the selected output directory.
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Here is a typical session
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```bash
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PS1:C:\Users\fred> invokeai
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* Initializing, be patient...
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* Initializing, be patient...
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>> Initialization file /home/lstein/invokeai/invokeai.init found. Loading...
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>> Internet connectivity is True
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>> InvokeAI, version 2.3.0-rc5
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>> InvokeAI runtime directory is "/home/lstein/invokeai"
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>> GFPGAN Initialized
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>> CodeFormer Initialized
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>> ESRGAN Initialized
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>> Using device_type cuda
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>> xformers memory-efficient attention is available and enabled
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(...more initialization messages...)
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* Initialization done! Awaiting your command (-h for help, 'q' to quit)
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invoke> ashley judd riding a camel -n2 -s150
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Outputs:
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outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
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outputs/img-samples/00010.png: "ashley judd riding a camel" -n2 -s150 -S 1362479620
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invoke> "there's a fly in my soup" -n6 -g
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outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
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seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
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invoke> q
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```
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## Arguments
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The script recognizes a series of command-line switches that will
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change important global defaults, such as the directory for image
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outputs and the location of the model weight files.
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### List of arguments recognized at the command line
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These command-line arguments can be passed to `invoke.py` when you first run it
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from the Windows, Mac or Linux command line. Some set defaults that can be
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overridden on a per-prompt basis (see
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[List of prompt arguments](#list-of-prompt-arguments). Others
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| Argument <img width="240" align="right"/> | Shortcut <img width="100" align="right"/> | Default <img width="320" align="right"/> | Description |
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| ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- |
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| `--help` | `-h` | | Print a concise help message. |
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| `--outdir <path>` | `-o<path>` | `outputs/img_samples` | Location for generated images. |
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| `--prompt_as_dir` | `-p` | `False` | Name output directories using the prompt text. |
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| `--from_file <path>` | | `None` | Read list of prompts from a file. Use `-` to read from standard input |
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| `--model <modelname>` | | `stable-diffusion-1.5` | Loads the initial model specified in configs/models.yaml. |
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| `--ckpt_convert ` | | `False` | If provided both .ckpt and .safetensors files will be auto-converted into diffusers format in memory |
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| `--autoconvert <path>` | | `None` | On startup, scan the indicated directory for new .ckpt/.safetensor files and automatically convert and import them |
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| `--precision` | | `fp16` | Provide `fp32` for full precision mode, `fp16` for half-precision. `fp32` needed for Macintoshes and some NVidia cards. |
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| `--png_compression <0-9>` | `-z<0-9>` | `6` | Select level of compression for output files, from 0 (no compression) to 9 (max compression) |
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| `--safety-checker` | | `False` | Activate safety checker for NSFW and other potentially disturbing imagery |
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| `--patchmatch`, `--no-patchmatch` | | `--patchmatch` | Load/Don't load the PatchMatch inpainting extension |
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| `--xformers`, `--no-xformers` | | `--xformers` | Load/Don't load the Xformers memory-efficient attention module (CUDA only) |
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| `--web` | | `False` | Start in web server mode |
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| `--host <ip addr>` | | `localhost` | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any. |
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| `--port <port>` | | `9090` | Which port web server should listen for requests on. |
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| `--config <path>` | | `configs/models.yaml` | Configuration file for models and their weights. |
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| `--iterations <int>` | `-n<int>` | `1` | How many images to generate per prompt. |
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| `--width <int>` | `-W<int>` | `512` | Width of generated image |
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| `--height <int>` | `-H<int>` | `512` | Height of generated image | `--steps <int>` | `-s<int>` | `50` | How many steps of refinement to apply |
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| `--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. |
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| `--fit` | `-F` | `False` | For img2img: scale the init image to fit into the specified -H and -W dimensions |
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| `--grid` | `-g` | `False` | Save all image series as a grid rather than individually. |
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| `--sampler <sampler>` | `-A<sampler>` | `k_lms` | Sampler to use. Use `-h` to get list of available samplers. |
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| `--seamless` | | `False` | Create interesting effects by tiling elements of the image. |
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| `--embedding_path <path>` | | `None` | Path to pre-trained embedding manager checkpoints, for custom models |
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| `--gfpgan_model_path` | | `experiments/pretrained_models/GFPGANv1.4.pth` | Path to GFPGAN model file. |
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| `--free_gpu_mem` | | `False` | Free GPU memory after sampling, to allow image decoding and saving in low VRAM conditions |
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| `--precision` | | `auto` | Set model precision, default is selected by device. Options: auto, float32, float16, autocast |
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!!! warning "These arguments are deprecated but still work"
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<div align="center" markdown>
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| Argument | Shortcut | Default | Description |
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|--------------------|------------|---------------------|--------------|
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| `--full_precision` | | `False` | Same as `--precision=fp32`|
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| `--weights <path>` | | `None` | Path to weights file; use `--model stable-diffusion-1.4` instead |
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| `--laion400m` | `-l` | `False` | Use older LAION400m weights; use `--model=laion400m` instead |
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</div>
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!!! tip
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On Windows systems, you may run into
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problems when passing the invoke script standard backslashed path
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names because the Python interpreter treats "\" as an escape.
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You can either double your slashes (ick): `C:\\path\\to\\my\\file`, or
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use Linux/Mac style forward slashes (better): `C:/path/to/my/file`.
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## The .invokeai initialization file
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To start up invoke.py with your preferred settings, place your desired
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startup options in a file in your home directory named `.invokeai` The
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file should contain the startup options as you would type them on the
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command line (`--steps=10 --grid`), one argument per line, or a
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mixture of both using any of the accepted command switch formats:
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!!! example "my unmodified initialization file"
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```bash title="~/.invokeai" linenums="1"
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# InvokeAI initialization file
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# This is the InvokeAI initialization file, which contains command-line default values.
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# Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
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# or renaming it and then running invokeai-configure again.
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# The --root option below points to the folder in which InvokeAI stores its models, configs and outputs.
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--root="/Users/mauwii/invokeai"
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# the --outdir option controls the default location of image files.
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--outdir="/Users/mauwii/invokeai/outputs"
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# You may place other frequently-used startup commands here, one or more per line.
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# Examples:
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# --web --host=0.0.0.0
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# --steps=20
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# -Ak_euler_a -C10.0
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```
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!!! note
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The initialization file only accepts the command line arguments.
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There are additional arguments that you can provide on the `invoke>` command
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line (such as `-n` or `--iterations`) that cannot be entered into this file.
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Also be alert for empty blank lines at the end of the file, which will cause
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an arguments error at startup time.
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## List of prompt arguments
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After the invoke.py script initializes, it will present you with a `invoke>`
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prompt. Here you can enter information to generate images from text
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([txt2img](#txt2img)), to embellish an existing image or sketch
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([img2img](#img2img)), or to selectively alter chosen regions of the image
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([inpainting](#inpainting)).
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### txt2img
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!!! example ""
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```bash
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invoke> waterfall and rainbow -W640 -H480
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```
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This will create the requested image with the dimensions 640 (width)
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and 480 (height).
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Here are the invoke> command that apply to txt2img:
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| Argument <img width="680" align="right"/> | Shortcut <img width="420" align="right"/> | Default <img width="480" align="right"/> | Description |
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| ----------------------------------------- | ----------------------------------------- | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| "my prompt" | | | Text prompt to use. The quotation marks are optional. |
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| `--width <int>` | `-W<int>` | `512` | Width of generated image |
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| `--height <int>` | `-H<int>` | `512` | Height of generated image |
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| `--iterations <int>` | `-n<int>` | `1` | How many images to generate from this prompt |
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| `--steps <int>` | `-s<int>` | `50` | How many steps of refinement to apply |
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| `--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 |
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| `--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. |
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| `--sampler <sampler>` | `-A<sampler>` | `k_lms` | Sampler to use. Use -h to get list of available samplers. |
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| `--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] |
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| `--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 |
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| `--png_compression <0-9>` | `-z<0-9>` | `6` | Select level of compression for output files, from 0 (no compression) to 9 (max compression) |
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| `--grid` | `-g` | `False` | Turn on grid mode to return a single image combining all the images generated by this prompt |
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| `--individual` | `-i` | `True` | Turn off grid mode (deprecated; leave off --grid instead) |
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| `--outdir <path>` | `-o<path>` | `outputs/img_samples` | Temporarily change the location of these images |
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| `--seamless` | | `False` | Activate seamless tiling for interesting effects |
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| `--seamless_axes` | | `x,y` | Specify which axes to use circular convolution on. |
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| `--log_tokenization` | `-t` | `False` | Display a color-coded list of the parsed tokens derived from the prompt |
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| `--skip_normalization` | `-x` | `False` | Weighted subprompts will not be normalized. See [Weighted Prompts](../features/OTHER.md#weighted-prompts) |
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| `--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. |
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| `--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) |
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| `--facetool <name>` | `-ft <name>` | `-ft gfpgan` | Select face restoration algorithm to use: gfpgan, codeformer |
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| `--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 |
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| `--save_original` | `-save_orig` | `False` | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced. |
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| `--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](../features/VARIATIONS.md). |
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| `--with_variations <pattern>` | | `None` | Combine two or more variations. See [Variations](../features/VARIATIONS.md) for now to use this. |
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| `--save_intermediates <n>` | | `None` | Save the image from every nth step into an "intermediates" folder inside the output directory |
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| `--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.) |
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| `--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.) |
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!!! note
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the width and height of the image must be multiples of 64. You can
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provide different values, but they will be rounded down to the nearest multiple
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of 64.
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!!! example "This is a example of img2img"
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```bash
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invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit
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```
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This will modify the indicated vacation photograph by making it more like the
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prompt. Results will vary greatly depending on what is in the image. We also ask
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to --fit the image into a box no bigger than 640x480. Otherwise the image size
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will be identical to the provided photo and you may run out of memory if it is
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large.
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In addition to the command-line options recognized by txt2img, img2img accepts
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additional options:
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| Argument <img width="160" align="right"/> | Shortcut | Default | Description |
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| ----------------------------------------- | ----------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `--init_img <path>` | `-I<path>` | `None` | Path to the initialization image |
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| `--fit` | `-F` | `False` | Scale the image to fit into the specified -H and -W dimensions |
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| `--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. |
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### inpainting
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!!! example ""
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```bash
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invoke> waterfall and rainbow -I./vacation-photo.png -M./vacation-mask.png -W640 -H480 --fit
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```
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This will do the same thing as img2img, but image alterations will
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only occur within transparent areas defined by the mask file specified
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by `-M`. You may also supply just a single initial image with the areas
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to overpaint made transparent, but you must be careful not to destroy
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the pixels underneath when you create the transparent areas. See
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[Inpainting](INPAINTING.md) for details.
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inpainting accepts all the arguments used for txt2img and img2img, as well as
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the --mask (-M) and --text_mask (-tm) arguments:
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| Argument <img width="100" align="right"/> | Shortcut | Default | Description |
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| ----------------------------------------- | ------------------------ | ------- | ------------------------------------------------------------------------------------------------ |
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| `--init_mask <path>` | `-M<path>` | `None` | Path to an image the same size as the initial_image, with areas for inpainting made transparent. |
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| `--invert_mask ` | | False | If true, invert the mask so that transparent areas are opaque and vice versa. |
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| `--text_mask <prompt> [<float>]` | `-tm <prompt> [<float>]` | <none> | Create a mask from a text prompt describing part of the image |
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The mask may either be an image with transparent areas, in which case the
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inpainting will occur in the transparent areas only, or a black and white image,
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in which case all black areas will be painted into.
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`--text_mask` (short form `-tm`) is a way to generate a mask using a text
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description of the part of the image to replace. For example, if you have an
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image of a breakfast plate with a bagel, toast and scrambled eggs, you can
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selectively mask the bagel and replace it with a piece of cake this way:
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```bash
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invoke> a piece of cake -I /path/to/breakfast.png -tm bagel
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```
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The algorithm uses <a
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href="https://github.com/timojl/clipseg">clipseg</a> to classify different
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regions of the image. The classifier puts out a confidence score for each region
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it identifies. Generally regions that score above 0.5 are reliable, but if you
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are getting too much or too little masking you can adjust the threshold down (to
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get more mask), or up (to get less). In this example, by passing `-tm` a higher
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value, we are insisting on a more stringent classification.
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```bash
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invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
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```
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### Custom Styles and Subjects
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You can load and use hundreds of community-contributed Textual
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Inversion models just by typing the appropriate trigger phrase. Please
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see [Concepts Library](../features/CONCEPTS.md) for more details.
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## Other Commands
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The CLI offers a number of commands that begin with "!".
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### Postprocessing images
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To postprocess a file using face restoration or upscaling, use the `!fix`
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command.
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#### `!fix`
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This command runs a post-processor on a previously-generated image. It takes a
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PNG filename or path and applies your choice of the `-U`, `-G`, or `--embiggen`
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switches in order to fix faces or upscale. If you provide a filename, the script
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will look for it in the current output directory. Otherwise you can provide a
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full or partial path to the desired file.
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Some examples:
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!!! example "Upscale to 4X its original size and fix faces using codeformer"
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```bash
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invoke> !fix 0000045.4829112.png -G1 -U4 -ft codeformer
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```
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!!! example "Use the GFPGAN algorithm to fix faces, then upscale to 3X using --embiggen"
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```bash
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invoke> !fix 0000045.4829112.png -G0.8 -ft gfpgan
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>> fixing outputs/img-samples/0000045.4829112.png
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>> retrieved seed 4829112 and prompt "boy enjoying a banana split"
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>> GFPGAN - Restoring Faces for image seed:4829112
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Outputs:
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[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
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```
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#### `!mask`
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This command takes an image, a text prompt, and uses the `clipseg` algorithm to
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automatically generate a mask of the area that matches the text prompt. It is
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useful for debugging the text masking process prior to inpainting with the
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`--text_mask` argument. See [INPAINTING.md] for details.
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### Model selection and importation
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The CLI allows you to add new models on the fly, as well as to switch
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among them rapidly without leaving the script. There are several
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different model formats, each described in the [Model Installation
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Guide](../installation/050_INSTALLING_MODELS.md).
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#### `!models`
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This prints out a list of the models defined in `config/models.yaml'. The active
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model is bold-faced
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Example:
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<pre>
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inpainting-1.5 not loaded Stable Diffusion inpainting model
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<b>stable-diffusion-1.5 active Stable Diffusion v1.5</b>
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waifu-diffusion not loaded Waifu Diffusion v1.4
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</pre>
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#### `!switch <model>`
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This quickly switches from one model to another without leaving the CLI script.
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`invoke.py` uses a memory caching system; once a model has been loaded,
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switching back and forth is quick. The following example shows this in action.
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Note how the second column of the `!models` table changes to `cached` after a
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model is first loaded, and that the long initialization step is not needed when
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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.
|