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https://github.com/invoke-ai/InvokeAI
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remove all references to CLI
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Lincoln Stein
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psychedelicious
psychedelicious
parent
9fa8e38163
commit
d27392cc2d
@ -28,8 +28,9 @@ by placing them in the designated directory for the compatible model type
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### An Example
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Here are a few examples to illustrate how it works. All these images were
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generated using the command-line client and the Stable Diffusion 1.5 model:
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Here are a few examples to illustrate how it works. All these images
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were generated using the legacy command-line client and the Stable
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Diffusion 1.5 model:
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| Japanese gardener | Japanese gardener <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
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| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
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@ -82,7 +82,7 @@ format of YAML files can be found
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[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
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You can fix a broken `invokeai.yaml` by deleting it and running the
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configuration script again -- option [7] in the launcher, "Re-run the
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configuration script again -- option [6] in the launcher, "Re-run the
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configure script".
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#### Reading Environment Variables
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@ -46,7 +46,7 @@ Diffuser-style ControlNet models are available at HuggingFace
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(http://huggingface.co) and accessed via their repo IDs (identifiers
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in the format "author/modelname"). The easiest way to install them is
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to use the InvokeAI model installer application. Use the
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`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
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`invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate
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to the CONTROLNETS section. Select the models you wish to install and
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press "APPLY CHANGES". You may also enter additional HuggingFace
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repo_ids in the "Additional models" textbox:
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@ -145,8 +145,8 @@ Additionally, each ControlNet section can be expanded in order to manipulate set
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#### Installation
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There are several ways to install IP-Adapter models with an existing InvokeAI installation:
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1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [5] to download models.
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2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.
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1. Through the command line interface launched from the invoke.sh / invoke.bat scripts, option [4] to download models.
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2. Through the Model Manager UI with models from the *Tools* section of [www.models.invoke.ai](https://www.models.invoke.ai). To do this, copy the repo ID from the desired model page, and paste it in the Add Model field of the model manager. **Note** Both the IP-Adapter and the Image Encoder must be installed for IP-Adapter to work. For example, the [SD 1.5 IP-Adapter](https://models.invoke.ai/InvokeAI/ip_adapter_plus_sd15) and [SD1.5 Image Encoder](https://models.invoke.ai/InvokeAI/ip_adapter_sd_image_encoder) must be installed to use IP-Adapter with SD1.5 based models.
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3. **Advanced -- Not recommended ** Manually downloading the IP-Adapter and Image Encoder files - Image Encoder folders shouid be placed in the `models\any\clip_vision` folders. IP Adapter Model folders should be placed in the relevant `ip-adapter` folder of relevant base model folder of Invoke root directory. For example, for the SDXL IP-Adapter, files should be added to the `model/sdxl/ip_adapter/` folder.
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#### Using IP-Adapter
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@ -16,9 +16,10 @@ Model Merging can be be done by navigating to the Model Manager and clicking the
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display all the diffusers-style models that InvokeAI knows about.
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If you do not see the model you are looking for, then it is probably
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a legacy checkpoint model and needs to be converted using the
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`invoke` command-line client and its `!optimize` command. You
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must select at least two models to merge. The third can be left at
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"None" if you desire.
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"Convert" option in the Web-based Model Manager tab.
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You must select at least two models to merge. The third can be left
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at "None" if you desire.
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* Alpha: This is the ratio to use when combining models. It ranges
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from 0 to 1. The higher the value, the more weight is given to the
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@ -8,7 +8,7 @@ title: Command-line Utilities
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InvokeAI comes with several scripts that are accessible via the
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command line. To access these commands, start the "developer's
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console" from the launcher (`invoke.bat` menu item [8]). Users who are
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console" from the launcher (`invoke.bat` menu item [7]). Users who are
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familiar with Python can alternatively activate InvokeAI's virtual
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environment (typically, but not necessarily `invokeai/.venv`).
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@ -34,7 +34,7 @@ invokeai-web --ram 7
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## **invokeai-merge**
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This is the model merge script, the same as launcher option [4]. Call
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This is the model merge script, the same as launcher option [3]. Call
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it with the `--gui` command-line argument to start the interactive
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console-based GUI. Alternatively, you can run it non-interactively
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using command-line arguments as illustrated in the example below which
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@ -48,7 +48,7 @@ invokeai-merge --force --base-model sd-1 --models stable-diffusion-1.5 inkdiffus
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## **invokeai-ti**
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This is the textual inversion training script that is run by launcher
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option [3]. Call it with `--gui` to run the interactive console-based
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option [2]. Call it with `--gui` to run the interactive console-based
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front end. It can also be run non-interactively. It has about a
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zillion arguments, but a typical training session can be launched
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with:
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@ -68,7 +68,7 @@ in Windows).
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## **invokeai-install**
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This is the console-based model install script that is run by launcher
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option [5]. If called without arguments, it will launch the
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option [4]. If called without arguments, it will launch the
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interactive console-based interface. It can also be used
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non-interactively to list, add and remove models as shown by these
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examples:
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@ -148,7 +148,7 @@ launch the web server against it with `invokeai-web --root InvokeAI-New`.
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## **invokeai-update**
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This is the interactive console-based script that is run by launcher
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menu item [9] to update to a new version of InvokeAI. It takes no
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menu item [8] to update to a new version of InvokeAI. It takes no
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command-line arguments.
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## **invokeai-metadata**
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@ -1,131 +0,0 @@
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---
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title: Variations
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---
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# :material-tune-variant: Variations
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## Intro
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InvokeAI's support for variations enables you to do the following:
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1. Generate a series of systematic variations of an image, given a prompt. The
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amount of variation from one image to the next can be controlled.
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2. Given two or more variations that you like, you can combine them in a
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weighted fashion.
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!!! Information ""
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This cheat sheet provides a quick guide for how this works in practice, using
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variations to create the desired image of Xena, Warrior Princess.
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## Step 1 -- Find a base image that you like
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The prompt we will use throughout is:
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`#!bash "lucy lawless as xena, warrior princess, character portrait, high resolution."`
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This will be indicated as `#!bash "prompt"` in the examples below.
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First we let SD create a series of images in the usual way, in this case
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requesting six iterations.
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<figure markdown>
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<figcaption> Seed 3357757885 looks nice </figcaption>
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</figure>
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---
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## Step 2 - Generating Variations
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Let's try to generate some variations on this image. We select the "*"
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symbol in the line of icons above the image in order to fix the prompt
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and seed. Then we open up the "Variations" section of the generation
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panel and use the slider to set the variation amount to 0.2. The
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higher this value, the more each generated image will differ from the
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previous one.
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Now we run the prompt a second time, requesting six iterations. You
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will see six images that are thematically related to each other. Try
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increasing and decreasing the variation amount and see what happens.
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### **Variation Sub Seeding**
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Note that the output for each image has a `-V` option giving the "variant
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subseed" for that image, consisting of a seed followed by the variation amount
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used to generate it.
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This gives us a series of closely-related variations, including the two shown
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here.
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<figure markdown>
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<figcaption>subseed 3647897225</figcaption>
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</figure>
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<figure markdown>
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<figcaption>subseed 1614299449</figcaption>
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</figure>
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I like the expression on Xena's face in the first one (subseed 3647897225), and
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the armor on her shoulder in the second one (subseed 1614299449). Can we combine
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them to get the best of both worlds?
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We combine the two variations using `-V` (`--with_variations`). Again, we must
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provide the seed for the originally-chosen image in order for this to work.
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```bash
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invoke> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1
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Outputs:
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./outputs/Xena/000003.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1 -S3357757885
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```
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Here we are providing equal weights (0.1 and 0.1) for both the subseeds. The
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resulting image is close, but not exactly what I wanted:
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<figure markdown>
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<figcaption> subseed 1614299449 </figcaption>
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</figure>
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We could either try combining the images with different weights, or we can
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generate more variations around the almost-but-not-quite image. We do the
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latter, using both the `-V` (combining) and `-v` (variation strength) options.
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Note that we use `-n6` to generate 6 variations:
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```bash
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invoke> "prompt" -S3357757885 -V3647897225,0.1,1614299449,0.1 -v0.05 -n6
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Outputs:
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./outputs/Xena/000004.3279757577.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3279757577:0.05 -S3357757885
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./outputs/Xena/000004.2853129515.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2853129515:0.05 -S3357757885
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./outputs/Xena/000004.3747154981.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,3747154981:0.05 -S3357757885
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./outputs/Xena/000004.2664260391.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2664260391:0.05 -S3357757885
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./outputs/Xena/000004.1642517170.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,1642517170:0.05 -S3357757885
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./outputs/Xena/000004.2183375608.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.1,1614299449:0.1,2183375608:0.05 -S3357757885
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```
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This produces six images, all slight variations on the combination of the chosen
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two images. Here's the one I like best:
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<figure markdown>
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<figcaption> subseed 3747154981 </figcaption>
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</figure>
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As you can see, this is a very powerful tool, which when combined with subprompt
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weighting, gives you great control over the content and quality of your
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generated images.
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## Variations and Samplers
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The sampler you choose has a strong effect on variation strength. Some
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samplers, such as `k_euler_a` are very "creative" and produce significant
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amounts of image-to-image variation even when the seed is fixed and the
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`-v` argument is very low. Others are more deterministic. Feel free to
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experiment until you find the combination that you like.
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Also be aware of the [Perlin Noise](OTHER.md#thresholding-and-perlin-noise-initialization-options)
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feature, which provides another way of introducing variability into your
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image generation requests.
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@ -28,7 +28,7 @@ Learn how to install and use ControlNet models for fine control over
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image output.
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### * [Image-to-Image Guide](IMG2IMG.md)
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Use a seed image to build new creations in the CLI.
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Use a seed image to build new creations.
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## Model Management
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Reference in New Issue
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