--- title: CLI hide: - toc --- # :material-bash: CLI ## **Interactive Command Line Interface** The `invoke.py` script, located in `scripts/`, provides an interactive interface to image generation similar to the "invoke mothership" bot that Stable AI provided on its Discord server. Unlike the `txt2img.py` and `img2img.py` scripts provided in the original [CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) source code repository, the time-consuming initialization of the AI model initialization only happens once. After that image generation from the command-line interface is very fast. 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. In addition, as of version 1.02, it also writes the prompt into the PNG file's metadata where it can be retrieved using `scripts/images2prompt.py` The script is confirmed to work on Linux, Windows and Mac systems. !!! note This script runs from the command-line or can be used as a Web application. The Web GUI is currently rudimentary, but a much better replacement is on its way. ```bash (invokeai) ~/stable-diffusion$ python3 ./scripts/invoke.py * Initializing, be patient... Loading model from models/ldm/text2img-large/model.ckpt (...more initialization messages...) * Initialization done! Awaiting your command... 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 # this shows how to retrieve the prompt stored in the saved image's metadata (invokeai) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png 00009.png: "ashley judd riding a camel" -s150 -S 416354203 00010.png: "ashley judd riding a camel" -s150 -S 1362479620 00011.png: "there's a fly in my soup" -n6 -g -S 2685670268 ``` ![invoke-py-demo](../assets/dream-py-demo.png) The `invoke>` prompt's arguments are pretty much identical to those used in the Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you do). A significant change is that creation of individual images is now the default unless `--grid` (`-g`) is given. A full list is given in [List of prompt arguments](#list-of-prompt-arguments). ## Arguments The script itself also 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 | Shortcut | Default | Description | | ----------------------------------------- | ----------------------------------------- | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------- | | `--help` | `-h` | | Print a concise help message. | | `--outdir ` | `-o` | `outputs/img_samples` | Location for generated images. | | `--prompt_as_dir` | `-p` | `False` | Name output directories using the prompt text. | | `--from_file ` | | `None` | Read list of prompts from a file. Use `-` to read from standard input | | `--model ` | | `stable-diffusion-1.4` | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" | | `--full_precision` | `-F` | `False` | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards. | | `--png_compression <0-9>` | `-z<0-9>` | 6 | Select level of compression for output files, from 0 (no compression) to 9 (max compression) | | `--safety-checker` | | False | Activate safety checker for NSFW and other potentially disturbing imagery | | `--web` | | `False` | Start in web server mode | | `--host ` | | `localhost` | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any. | | `--port ` | | `9090` | Which port web server should listen for requests on. | | `--config ` | | `configs/models.yaml` | Configuration file for models and their weights. | | `--iterations ` | `-n` | `1` | How many images to generate per prompt. | | `--grid` | `-g` | `False` | Save all image series as a grid rather than individually. | | `--sampler ` | `-A` | `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 ` | | `None` | Path to pre-trained embedding manager checkpoints, for custom models | | `--gfpgan_dir` | | `src/gfpgan` | Path to where GFPGAN is installed. | | `--gfpgan_model_path` | | `experiments/pretrained_models/GFPGANv1.4.pth` | Path to GFPGAN model file, relative to `--gfpgan_dir`. | | `--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 deprecated These arguments are deprecated but still work:
| Argument | Shortcut | Default | Description | |--------------------|------------|---------------------|--------------| | `--weights ` | | `None` | Pth to weights file; use `--model stable-diffusion-1.4` instead | | `--laion400m` | `-l` | `False` | Use older LAION400m weights; use `--model=laion400m` instead |
!!! 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`. ## 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 | Shortcut | Default | Description | |--------------------|------------|---------------------|--------------| | "my prompt" | | | Text prompt to use. The quotation marks are optional. | | --width | -W | 512 | Width of generated image | | --height | -H | 512 | Height of generated image | | --iterations | -n | 1 | How many images to generate from this prompt | | --steps | -s | 50 | How many steps of refinement to apply | | --cfg_scale | -C | 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 | -S | None | Set the random seed for the next series of images. This can be used to recreate an image generated previously.| | --sampler | -A| k_lms | Sampler to use. Use -h to get list of available samplers. | | --karras_max | | 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 | -o | outputs/img_samples | Temporarily change the location of these images | | --seamless | | False | Activate seamless tiling for interesting effects | | --seamless_axes | | x,y | Specify which axes to use circular convolution on. | | --log_tokenization | -t | False | Display a color-coded list of the parsed tokens derived from the prompt | | --skip_normalization| -x | False | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts) | | --upscale | -U | -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 | -G | -G0 | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0) | | --facetool | -ft | -ft gfpgan | Select face restoration algorithm to use: gfpgan, codeformer | | --codeformer_fidelity | -cf | 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 |-v| 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 and -n to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). | | --with_variations | | None | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this. | | --save_intermediates | | None | Save the image from every nth step into an "intermediates" folder inside the output directory | Note that the width and height of the image must be multiples of 64. You can provide different values, but they will be rounded down to the nearest multiple of 64. ### This is an example of img2img: ~~~~ invoke> waterfall and rainbow -I./vacation-photo.png -W640 -H480 --fit ~~~~ This will modify the indicated vacation photograph by making it more like the prompt. Results will vary greatly depending on what is in the image. We also ask to --fit the image into a box no bigger than 640x480. Otherwise the image size will be identical to the provided photo and you may run out of memory if it is large. In addition to the command-line options recognized by txt2img, img2img accepts additional options: | Argument | Shortcut | Default | Description | |----------------------|-------------|-----------------|--------------| | `--init_img ` | `-I` | `None` | Path to the initialization image | | `--fit` | `-F` | `False` | Scale the image to fit into the specified -H and -W dimensions | | `--strength ` | `-s` | `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 | Shortcut | Default | Description | |--------------------|------------|---------------------|--------------| | `--init_mask ` | `-M` | `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 []` | `-tm []` | | 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: ~~~ invoke> a piece of cake -I /path/to/breakfast.png -tm bagel ~~~ The algorithm uses clipseg to classify different regions of the image. The classifier puts out a confidence score for each region it identifies. Generally regions that score above 0.5 are reliable, but if you are getting too much or too little masking you can adjust the threshold down (to get more mask), or up (to get less). In this example, by passing `-tm` a higher value, we are insisting on a more stringent classification. ~~~ invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6 ~~~ # Other Commands 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. ### !models This prints out a list of the models defined in `config/models.yaml'. The active model is bold-faced Example: 
laion400m                 not loaded  
stable-diffusion-1.4          active  Stable Diffusion v1.4
waifu-diffusion           not loaded  Waifu Diffusion v1.3
### !switch 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. 
invoke> !models
laion400m                 not loaded  
stable-diffusion-1.4          cached  Stable Diffusion v1.4
waifu-diffusion               active  Waifu Diffusion v1.3

invoke> !switch waifu-diffusion
>> Caching model stable-diffusion-1.4 in system RAM
>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
   | LatentDiffusion: Running in eps-prediction mode
   | DiffusionWrapper has 859.52 M params.
   | Making attention of type 'vanilla' with 512 in_channels
   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
   | Making attention of type 'vanilla' with 512 in_channels
   | Using faster float16 precision
>> Model loaded in 18.24s
>> Max VRAM used to load the model: 2.17G 
>> Current VRAM usage:2.17G
>> Setting Sampler to k_lms

invoke> !models
laion400m                 not loaded  
stable-diffusion-1.4          cached  Stable Diffusion v1.4
waifu-diffusion               active  Waifu Diffusion v1.3

invoke> !switch stable-diffusion-1.4
>> Caching model waifu-diffusion in system RAM
>> Retrieving model stable-diffusion-1.4 from system RAM cache
>> Setting Sampler to k_lms

invoke> !models
laion400m                 not loaded  
stable-diffusion-1.4          active  Stable Diffusion v1.4
waifu-diffusion               cached  Waifu Diffusion v1.3
### !import_model 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. Example: 
invoke> !import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
>> Model import in process. Please enter the values needed to configure this model:

Name for this model: waifu-diffusion
Description of this model: Waifu Diffusion v1.3
Configuration file for this model: configs/stable-diffusion/v1-inference.yaml
Default image width: 512
Default image height: 512
>> New configuration:
waifu-diffusion:
  config: configs/stable-diffusion/v1-inference.yaml
  description: Waifu Diffusion v1.3
  height: 512
  weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
  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-epoch08-float16.ckpt
   | LatentDiffusion: Running in eps-prediction mode
   | DiffusionWrapper has 859.52 M params.
   | Making attention of type 'vanilla' with 512 in_channels
   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
   | Making attention of type 'vanilla' with 512 in_channels
   | Using faster float16 precision
invoke>
###!edit_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: 
invoke> !edit_model waifu-diffusion
>> Editing model waifu-diffusion from configuration file ./configs/models.yaml
description: Waifu diffusion v1.4beta
weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt
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
...
======= invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3 ...lots of text... Outputs: [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix "outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512 -H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25 ``` ## History processing The CLI provides a series of convenient commands for reviewing previous actions, retrieving them, modifying them, and re-running them. ### !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: ```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. This example loads 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 ``` This one fetches the generation commands from a batch of files and stores them into `selected.txt`: ```bash invoke> !fetch outputs\selected-imgs\*.png selected.txt ``` ### !replay This command replays a text file generated by !fetch or created manually ~~~ invoke> !replay outputs\selected-imgs\selected.txt ~~~ Note that these commands may behave unexpectedly if given a PNG file that was not generated by InvokeAI. ### !search 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 -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.