-
+
+
+
+
-Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains.
+Put simply: starting from a frame of fuzz/static, SD finds details in each frame that it thinks look like "fire" and brings them a little bit more into focus, gradually scrubbing out the fuzz until a clear image remains.
-**When you use `img2img`** some of the earlier steps are cut, and instead an initial image of your choice is used. But because of how the maths behind Stable Diffusion works, this image needs to be mixed with just the right amount of noise (fuzz/static) for where it is being inserted. This is where the strength parameter comes in. Depending on the set strength, your image will be inserted into the sequence at the appropriate point, with just the right amount of noise.
+**When you use `img2img`** some of the earlier steps are cut, and instead an initial image of your choice is used. But because of how the maths behind Stable Diffusion works, this image needs to be mixed with just the right amount of noise (fuzz/static) for where it is being inserted. This is where the strength parameter comes in. Depending on the set strength, your image will be inserted into the sequence at the appropriate point, with just the right amount of noise.
### A concrete example
-Say I want SD to draw a fire based on this hand-drawn image:
+I want SD to draw a fire based on this hand-drawn image:
+
+
Let's only do 10 steps, to make it easier to see what's happening. If strength is `0.7`, this is what the internal steps the algorithm has to take will look like:
-
+
+
+
With strength `0.4`, the steps look more like this:
-
+
+
+
Notice how much more fuzzy the starting image is for strength `0.7` compared to `0.4`, and notice also how much longer the sequence is with `0.7`:
| | strength = 0.7 | strength = 0.4 |
-| -- | -- | -- |
-| initial image that SD sees |  |  |
+| -- | :--: | :--: |
+| initial image that SD sees |  |  |
| steps argument to `dream>` | `-S10` | `-S10` |
| steps actually taken | 7 | 4 |
-| latent space at each step |  |  |
-| output |  |  |
+| latent space at each step |  |  |
+| output |  |  |
Both of the outputs look kind of like what I was thinking of. With the strength higher, my input becomes more vague, *and* Stable Diffusion has more steps to refine its output. But it's not really making what I want, which is a picture of cheery open fire. With the strength lower, my input is more clear, *but* Stable Diffusion has less chance to refine itself, so the result ends up inheriting all the problems of my bad drawing.
+If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `"fire"`:
-If you want to try this out yourself, all of these are using a seed of `1592514025` with a width/height of `384`, step count `10`, the default sampler (`k_lms`), and the single-word prompt `fire`:
-
-```commandline
+```bash
invoke> "fire" -s10 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png --strength 0.7
```
-The code for rendering intermediates is on my (damian0815's) branch [document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) - run `invoke.py` and check your `outputs/img-samples/intermediates` folder while generating an image.
+The code for rendering intermediates is on my (damian0815's) branch [document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) - run `invoke.py` and check your `outputs/img-samples/intermediates` folder while generating an image.
### Compensating for the reduced step count
@@ -117,37 +136,43 @@ After putting this guide together I was curious to see how the difference would
Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD does `20` steps from my image):
-```commandline
+```bash
invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
```
-
+
+
+
-and strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
+and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
-```commandline
+```bash
invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
```
-
+
+
+
In both cases the image is nice and clean and "finished", but because at strength `0.7` Stable Diffusion has been give so much more freedom to improve on my badly-drawn flames, they've come out looking much better. You can really see the difference when looking at the latent steps. There's more noise on the first image with strength `0.7`:
-
+
than there is for strength `0.4`:
-
+
-and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image.
+and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image.
Unfortunately, it seems that `img2img` is very sensitive to the step count. Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
-
+
+
+
By comparing the latents we can sort of see that something got interpreted differently enough on the third or fourth step to lead to a rather different interpretation of the flames.
-
-
+
+
-This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see https://huggingface.co/blog/stable_diffusion for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.
+This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see [stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.
diff --git a/docs/features/INPAINTING.md b/docs/features/INPAINTING.md
index 38c7c8d397..8a4ef96a82 100644
--- a/docs/features/INPAINTING.md
+++ b/docs/features/INPAINTING.md
@@ -35,42 +35,43 @@ We are hoping to get rid of the need for this workaround in an upcoming release.
[GIMP](https://www.gimp.org/) is a popular Linux photoediting tool.
1. Open image in GIMP.
-2. Layer->Transparency->Add Alpha Channel
+2. Layer --> Transparency --> Add Alpha Channel
3. Use lasoo 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"
+4. Choose Select --> Float to create a floating selection
+5. Open the Layers toolbar (++ctrl+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
-
+ 
2. Use any of the selection tools (Marquee, Lasso, or Wand) to select the area you desire to inpaint.
-
+ 
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 undrlying image, or your inpainting results will be dramatically impacted.
-
+ 
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.
-
+ 
-6. Save the image as a transparent PNG by using the "Save a Copy" option in the File menu, or using the Alt + Ctrl + S keyboard shortcut
+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++
-
+ 
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!
-
+ 
8. In the export dialogue, Make sure the "Save colour values from transparent pixels" checkbox is selected.
diff --git a/docs/features/OTHER.md b/docs/features/OTHER.md
index b0f1b3f062..2ee8661b3f 100644
--- a/docs/features/OTHER.md
+++ b/docs/features/OTHER.md
@@ -6,13 +6,9 @@ title: Others
## **Google Colab**
-Stable Diffusion AI Notebook:
-Open and follow instructions to use an isolated environment running -Dream.
+Stable Diffusion AI Notebook: [](https://colab.research.google.com/github/lstein/stable-diffusion/blob/main/notebooks/Stable_Diffusion_AI_Notebook.ipynb) + +Open and follow instructions to use an isolated environment running Dream. Output Example:  @@ -71,24 +67,30 @@ combination of integers and floating point numbers, and they do not need to add --- -## Thresholding and Perlin Noise Initialization Options +## **Thresholding and Perlin Noise Initialization Options** Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images. -For better intuition into what these options do in practice, [here is a graphic demonstrating them both](../assets/truncation_comparison.jpg) in use. In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from +For better intuition into what these options do in practice: + + + +In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are fixed, so the initial prompt is as follows (no thresholding or perlin noise): -``` - a portrait of a beautiful young lady -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0 +```bash +invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0 ``` Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2: -``` - a portrait of a beautiful young lady -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2 +```bash +invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2 ``` -Note: currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization. +!!! note + + currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization. --- diff --git a/docs/features/POSTPROCESS.md b/docs/features/POSTPROCESS.md index 8503e1f0f2..9d9ff5f800 100644 --- a/docs/features/POSTPROCESS.md +++ b/docs/features/POSTPROCESS.md @@ -1,8 +1,9 @@ - --- title: Postprocessing --- +# :material-image-edit: Postprocessing + ## Intro This extension provides the ability to restore faces and upscale diff --git a/docs/features/TEXTUAL_INVERSION.md b/docs/features/TEXTUAL_INVERSION.md index 15ac0891ad..728ddfd2bf 100644 --- a/docs/features/TEXTUAL_INVERSION.md +++ b/docs/features/TEXTUAL_INVERSION.md @@ -1,8 +1,8 @@ --- -title: TEXTUAL_INVERSION +title: Textual Inversion --- -# :material-file-document-plus-outline: TEXTUAL_INVERSION +# :material-file-document-plus-outline: Textual Inversion ## **Personalizing Text-to-Image Generation** diff --git a/docs/features/WEB.md b/docs/features/WEB.md index a1e4458c8f..e7362a44e4 100644 --- a/docs/features/WEB.md +++ b/docs/features/WEB.md @@ -2,6 +2,8 @@ title: InvokeAI Web Server --- +# :material-web: InvokeAI Web Server + As of version 2.0.0, this distribution comes with a full-featured web server (see screenshot). To use it, run the `invoke.py` script by adding the `--web` option: @@ -294,9 +296,9 @@ were not covered here. The WebGUI is only rapid development. Check back regularly for updates! -# Reference +## Reference -## Additional Options +### Additional Options `--web_develop` - Starts the web server in development mode. `--web_verbose` - Enables verbose logging @@ -311,36 +313,41 @@ updates! `--gui` - Start InvokeAI GUI - This is the "desktop mode" version of the web app. It uses Flask to create a desktop app experience of the webserver. - -## Web Specific Features +### Web Specific Features The web experience offers an incredibly easy-to-use experience for interacting with the InvokeAI toolkit. For detailed guidance on individual features, see the Feature-specific help documents available in this directory. Note that the latest functionality available in the CLI may not always be available in the Web interface. -### Dark Mode & Light Mode +#### Dark Mode & Light Mode + The InvokeAI interface is available in a nano-carbon black & purple Dark Mode, and a "burn your eyes out Nosferatu" Light Mode. These can be toggled by clicking the Sun/Moon icons at the top right of the interface.   -### Invocation Toolbar -The left side of the InvokeAI interface is available for customizing the prompt and the settings used for invoking your new image. Typing your prompt into the open text field and clicking the Invoke button will produce the image based on the settings configured in the toolbar. +#### Invocation Toolbar + +The left side of the InvokeAI interface is available for customizing the prompt and the settings used for invoking your new image. Typing your prompt into the open text field and clicking the Invoke button will produce the image based on the settings configured in the toolbar. See below for additional documentation related to each feature: + - [Core Prompt Settings](./CLI.md) - [Variations](./VARIATIONS.md) -- [Upscaling](./UPSCALE.md) +- [Upscaling](./POSTPROCESS.md#upscaling) - [Image to Image](./IMG2IMG.md) - [Inpainting](./INPAINTING.md) - [Other](./OTHER.md) -### Invocation Gallery +#### Invocation Gallery + The currently selected --outdir (or the default outputs folder) will display all previously generated files on load. As new invocations are generated, these will be dynamically added to the gallery, and can be previewed by selecting them. Each image also has a simple set of actions (e.g., Delete, Use Seed, Use All Parameters, etc.) that can be accessed by hovering over the image. -### Image Workspace +#### Image Workspace + When an image from the Invocation Gallery is selected, or is generated, the image will be displayed within the center of the interface. A quickbar of common image interactions are displayed along the top of the image, including: + - Use image in the `Image to Image` workflow - Initialize Face Restoration on the selected file - Initialize Upscaling on the selected file diff --git a/docs/help/SAMPLER_CONVERGENCE.md b/docs/help/SAMPLER_CONVERGENCE.md index 5dfee5dc4e..d9bd58910e 100644 --- a/docs/help/SAMPLER_CONVERGENCE.md +++ b/docs/help/SAMPLER_CONVERGENCE.md @@ -1,8 +1,8 @@ --- -title: SAMPLER CONVERGENCE +title: Sampler Convergence --- -## *Sampler Convergence* +# :material-palette-advanced: *Sampler Convergence* As features keep increasing, making the right choices for your needs can become increasingly difficult. What sampler to use? And for how many steps? Do you change the CFG value? Do you use prompt weighting? Do you allow variations? @@ -21,6 +21,8 @@ Looking for a short version? Here's a TL;DR in 3 tables. | `K_HEUN` and `K_DPM_2` converge in less steps (but are slower). | | `K_DPM_2_A` and `K_EULER_A` incorporate a lot of creativity/variability. | +
+
| Sampler | (3 sample avg) it/s (M1 Max 64GB, 512x512) |
|---|---|
| `DDIM` | 1.89 |
@@ -32,6 +34,8 @@ Looking for a short version? Here's a TL;DR in 3 tables.
| `K_DPM_2_A` | 0.95 (slower) |
| `K_EULER_A` | 1.86 |
+
+
| Suggestions |
|:---|
| For most use cases, `K_LMS`, `K_HEUN` and `K_DPM_2` are the best choices (the latter 2 run 0.5x as quick, but tend to converge 2x as quick as `K_LMS`). At very low steps (≤ `-s8`), `K_HEUN` and `K_DPM_2` are not recommended. Use `K_LMS` instead.|
diff --git a/docs/index.md b/docs/index.md
index f053df25af..c9a19a18ce 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,6 +1,5 @@
---
title: Home
-template: main.html
---
-# :material-script-text-outline: InvokeAI: A Stable Diffusion Toolkit
+# ^^**InvokeAI: A Stable Diffusion Toolkit**^^ :tools:
Formally known as lstein/stable-diffusion  @@ -29,8 +28,8 @@ template: main.html [CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment [CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github [CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml -[discord badge]: https://flat.badgen.net/discord/members/htRgbc7e?icon=discord -[discord link]: https://discord.com/invite/htRgbc7e +[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord +[discord link]: https://discord.gg/ZmtBAhwWhy [github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github [github forks link]: https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion [github open issues badge]: https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github @@ -53,14 +52,13 @@ various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. -Quick links: -
+
+!!! note
+
+ This fork is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
## :octicons-package-dependencies-24: Installation
@@ -98,7 +96,7 @@ You wil need one of the following:
To run in full-precision mode, start `invoke.py` with the `--full_precision` flag:
```bash
- (ldm) ~/stable-diffusion$ python scripts/invoke.py --full_precision
+ (invokeai) ~/InvokeAI$ python scripts/invoke.py --full_precision
```
## :octicons-log-16: Latest Changes
diff --git a/docs/installation/INSTALL_DOCKER.md b/docs/installation/INSTALL_DOCKER.md
index 880b216f3c..eb2e2ab39f 100644
--- a/docs/installation/INSTALL_DOCKER.md
+++ b/docs/installation/INSTALL_DOCKER.md
@@ -1,4 +1,10 @@
-# Before you begin
+---
+title: Docker
+---
+
+# :fontawesome-brands-docker: Docker
+
+## Before you begin
- For end users: Install Stable Diffusion locally using the instructions for
your OS.
@@ -6,7 +12,7 @@
deployment to other environments (on-premises or cloud), follow these
instructions. For general use, install locally to leverage your machine's GPU.
-# Why containers?
+## Why containers?
They provide a flexible, reliable way to build and deploy Stable Diffusion.
You'll also use a Docker volume to store the largest model files and image
@@ -26,11 +32,11 @@ development purposes it's fine. Once you're done with development tasks on your
laptop you can build for the target platform and architecture and deploy to
another environment with NVIDIA GPUs on-premises or in the cloud.
-# Installation on a Linux container
+## Installation on a Linux container
-## Prerequisites
+### Prerequisites
-### Get the data files
+#### Get the data files
Go to
[Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
@@ -44,14 +50,14 @@ cd ~/Downloads
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
```
-### Install [Docker](https://github.com/santisbon/guides#docker)
+#### Install [Docker](https://github.com/santisbon/guides#docker)
On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
CPUs and Memory to avoid this
[Issue](https://github.com/invoke-ai/InvokeAI/issues/342). You may need to
increase Swap and Disk image size too.
-## Setup
+### Setup
Set the fork you want to use and other variables.
@@ -132,9 +138,9 @@ docker run -it \
$TAG_STABLE_DIFFUSION
```
-# Usage (time to have fun)
+## Usage (time to have fun)
-## Startup
+### Startup
If you're on a **Linux container** the `invoke` script is **automatically
started** and the output dir set to the Docker volume you created earlier.
@@ -158,7 +164,7 @@ invoke> -h
invoke> q
```
-## Text to Image
+### Text to Image
For quick (but bad) image results test with 5 steps (default 50) and 1 sample
image. This will let you know that everything is set up correctly.
@@ -188,7 +194,7 @@ volume):
docker cp dummy:/data/000001.928403745.png /Users//Pictures
```
-## Image to Image
+### Image to Image
You can also do text-guided image-to-image translation. For example, turning a
sketch into a detailed drawing.
@@ -225,7 +231,7 @@ If you're on a Linux container on your Mac
invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1
```
-## Web Interface
+### Web Interface
You can use the `invoke` script with a graphical web interface. Start the web
server with:
@@ -238,7 +244,7 @@ If it's running on your Mac point your Mac web browser to http://127.0.0.1:9090
Press Control-C at the command line to stop the web server.
-## Notes
+### Notes
Some text you can add at the end of the prompt to make it very pretty:
diff --git a/docs/installation/INSTALL_LINUX.md b/docs/installation/INSTALL_LINUX.md
index ca406bf0a5..629175c3fa 100644
--- a/docs/installation/INSTALL_LINUX.md
+++ b/docs/installation/INSTALL_LINUX.md
@@ -26,38 +26,36 @@ title: Linux
3. Copy the InvokeAI source code from GitHub:
-```
-(base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
-```
+ ```bash
+ (base) ~$ git clone https://github.com/invoke-ai/InvokeAI.git
+ ```
-This will create InvokeAI folder where you will follow the rest of the steps.
+ This will create InvokeAI folder where you will follow the rest of the steps.
4. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
-```
-(base) ~$ cd InvokeAI
-(base) ~/InvokeAI$
-```
+ ```bash
+ (base) ~$ cd InvokeAI
+ (base) ~/InvokeAI$
+ ```
5. Use anaconda to copy necessary python packages, create a new python
environment named `invokeai` and activate the environment.
-
-```
-(base) ~/InvokeAI$ conda env create
-(base) ~/InvokeAI$ conda activate invokeai
-(invokeai) ~/InvokeAI$
-```
+ ```bash
+ (base) ~/InvokeAI$ conda env create
+ (base) ~/InvokeAI$ conda activate invokeai
+ (invokeai) ~/InvokeAI$
+ ```
After these steps, your command prompt will be prefixed by `(invokeai)` as shown
above.
6. Load a couple of small machine-learning models required by stable diffusion:
-
-```
-(invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
-```
+ ```bash
+ (invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
+ ```
!!! note
@@ -79,33 +77,31 @@ This will create InvokeAI folder where you will follow the rest of the steps.
This will create a symbolic link from the stable-diffusion model.ckpt file, to
the true location of the `sd-v1-4.ckpt` file.
-
-```
-(invokeai) ~/InvokeAI$ mkdir -p models/ldm/stable-diffusion-v1
-(invokeai) ~/InvokeAI$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
-```
+ ```bash
+ (invokeai) ~/InvokeAI$ mkdir -p models/ldm/stable-diffusion-v1
+ (invokeai) ~/InvokeAI$ ln -sf /path/to/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
+ ```
8. Start generating images!
-```
-# for the pre-release weights use the -l or --liaon400m switch
-(invokeai) ~/InvokeAI$ python3 scripts/invoke.py -l
+ ```bash
+ # for the pre-release weights use the -l or --liaon400m switch
+ (invokeai) ~/InvokeAI$ python3 scripts/invoke.py -l
-# for the post-release weights do not use the switch
-(invokeai) ~/InvokeAI$ python3 scripts/invoke.py
+ # for the post-release weights do not use the switch
+ (invokeai) ~/InvokeAI$ python3 scripts/invoke.py
-# for additional configuration switches and arguments, use -h or --help
-(invokeai) ~/InvokeAI$ python3 scripts/invoke.py -h
-```
+ # for additional configuration switches and arguments, use -h or --help
+ (invokeai) ~/InvokeAI$ python3 scripts/invoke.py -h
+ ```
9. Subsequently, to relaunch the script, be sure to run "conda activate invokeai" (step 5, second command), enter the `InvokeAI` directory, and then launch the invoke script (step 8). If you forget to activate the 'invokeai' environment, the script will fail with multiple `ModuleNotFound` errors.
## Updating to newer versions of the script
-
This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI` and type:
-```
+```bash
(invokeai) ~/InvokeAI$ git pull
(invokeai) ~/InvokeAI$ conda env update -f environment.yml
```
diff --git a/docs/installation/INSTALL_MAC.md b/docs/installation/INSTALL_MAC.md
index 75e8f22785..086739e59e 100644
--- a/docs/installation/INSTALL_MAC.md
+++ b/docs/installation/INSTALL_MAC.md
@@ -2,6 +2,8 @@
title: macOS
---
+# :fontawesome-brands-apple: macOS
+
Invoke AI runs quite well on M1 Macs and we have a number of M1 users
in the community.
@@ -26,98 +28,120 @@ First you need to download a large checkpoint file.
While that is downloading, open Terminal and run the following commands one at a time, reading the comments and taking care to run the appropriate command for your Mac's architecture (Intel or M1).
-Do not just copy and paste the whole thing into your terminal!
+!!! todo "Homebrew"
-```bash
-# Install brew (and Xcode command line tools):
-/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+ If you have no brew installation yet (otherwise skip):
-# Now there are two options to get the Python (miniconda) environment up and running:
-# 1. Alongside pyenv
-# 2. Standalone
-#
-# If you don't know what we are talking about, choose 2.
-#
-# If you are familiar with python environments, you'll know there are other options
-# for setting up the environment - you are on your own if you go one of those routes.
+ ```bash title="install brew (and Xcode command line tools)"
+ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
+ ```
-##### BEGIN TWO DIFFERENT OPTIONS #####
+!!! todo "Conda Installation"
-### BEGIN OPTION 1: Installing alongside pyenv ###
-brew install pyenv-virtualenv # you might have this from before, no problem
-pyenv install anaconda3-2022.05
-pyenv virtualenv anaconda3-2022.05
-eval "$(pyenv init -)"
-pyenv activate anaconda3-2022.05
-### END OPTION 1 ###
+ Now there are two different ways to set up the Python (miniconda) environment:
-### BEGIN OPTION 2: Installing standalone ###
-# Install cmake, protobuf, and rust:
-brew install cmake protobuf rust
+ 1. Standalone
+ 2. with pyenv
-# BEGIN ARCHITECTURE-DEPENDENT STEP #
-# For M1: install miniconda (M1 arm64 version):
-curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
-/bin/bash Miniconda3-latest-MacOSX-arm64.sh
+ If you don't know what we are talking about, choose Standalone. If you are familiar with python environments, choose "with pyenv"
-# For Intel: install miniconda (Intel x86-64 version):
-curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
-/bin/bash Miniconda3-latest-MacOSX-x86_64.sh
-# END ARCHITECTURE-DEPENDENT STEP #
+ === "Standalone"
-### END OPTION 2 ###
+ ```bash title="Install cmake, protobuf, and rust"
+ brew install cmake protobuf rust
+ ```
-##### END TWO DIFFERENT OPTIONS #####
+ Then choose the kind of your Mac and install miniconda:
-# Clone the Invoke AI repo
-git clone https://github.com/invoke-ai/InvokeAI.git
-cd InvokeAI
+ === "M1 arm64"
-### WAIT FOR THE CHECKPOINT FILE TO DOWNLOAD, THEN PROCEED ###
+ ```bash title="Install miniconda for M1 arm64"
+ curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh \
+ -o Miniconda3-latest-MacOSX-arm64.sh
+ /bin/bash Miniconda3-latest-MacOSX-arm64.sh
+ ```
-# We will leave the big checkpoint wherever you stashed it for long-term storage,
-# and make a link to it from the repo's folder. This allows you to use it for
-# other repos, and if you need to delete Invoke AI, you won't have to download it again.
+ === "Intel x86_64"
-# Make the directory in the repo for the symlink
-mkdir -p models/ldm/stable-diffusion-v1/
+ ```bash title="Install miniconda for Intel"
+ curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh \
+ -o Miniconda3-latest-MacOSX-x86_64.sh
+ /bin/bash Miniconda3-latest-MacOSX-x86_64.sh
+ ```
-# This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
-PATH_TO_CKPT="$HOME/Downloads"
+ === "with pyenv"
-# Create a link to the checkpoint
-ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
+ ```bash
+ brew install pyenv-virtualenv
+ pyenv install anaconda3-2022.05
+ pyenv virtualenv anaconda3-2022.05
+ eval "$(pyenv init -)"
+ pyenv activate anaconda3-2022.05
+ ```
-# BEGIN ARCHITECTURE-DEPENDENT STEP #
-# For M1: Create the environment & install packages
-PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
+!!! todo "Clone the Invoke AI repo"
-# For Intel: Create the environment & install packages
-PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
+ ```bash
+ git clone https://github.com/invoke-ai/InvokeAI.git
+ cd InvokeAI
+ ```
-# END ARCHITECTURE-DEPENDENT STEP #
+!!! todo "Wait until the checkpoint-file download finished, then proceed"
-# Activate the environment (you need to do this every time you want to run SD)
-conda activate invokeai
+ We will leave the big checkpoint wherever you stashed it for long-term storage,
+ and make a link to it from the repo's folder. This allows you to use it for
+ other repos, or if you need to delete Invoke AI, you won't have to download it again.
-# This will download some bits and pieces and make take a while
-python scripts/preload_models.py
+ ```{.bash .annotate}
+ # Make the directory in the repo for the symlink
+ mkdir -p models/ldm/stable-diffusion-v1/
-# Run SD!
-python scripts/dream.py
-```
-# or run the web interface!
-python scripts/invoke.py --web
+ # This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
+ PATH_TO_CKPT="$HOME/Downloads" # (1)!
-# The original scripts should work as well.
-python scripts/orig_scripts/txt2img.py \
- --prompt "a photograph of an astronaut riding a horse" \
- --plms
-```
+ # Create a link to the checkpoint
+ ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
+ ```
-Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
-create -f environment-mac.yml` never finishing in some situations. So
-it isn't required but wont hurt.
+ 1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)
+
+!!! todo "Create the environment & install packages"
+
+ === "M1 Mac"
+
+ ```bash
+ PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yml
+ ```
+
+ === "Intel x86_64 Mac"
+
+ ```bash
+ PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-64 conda env create -f environment-mac.yml
+ ```
+
+ ```bash
+ # Activate the environment (you need to do this every time you want to run SD)
+ conda activate invokeai
+
+ # This will download some bits and pieces and make take a while
+ python scripts/preload_models.py
+
+ # Run SD!
+ python scripts/dream.py
+
+ # or run the web interface!
+ python scripts/invoke.py --web
+
+ # The original scripts should work as well.
+ python scripts/orig_scripts/txt2img.py \
+ --prompt "a photograph of an astronaut riding a horse" \
+ --plms
+ ```
+ !!! info
+
+ `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env
+ create -f environment-mac.yml` never finishing in some situations. So
+ it isn't required but wont hurt.
---
## Common problems
@@ -157,7 +181,6 @@ conda install \
-n ldm
```
-
If it takes forever to run `conda env create -f environment-mac.yml`, try this:
```bash
@@ -169,12 +192,12 @@ conda clean \
Or you could try to completley reset Anaconda:
- ```bash
- conda update \
- --force-reinstall \
- -y \
- -n base \
- -c defaults conda
+```bash
+conda update \
+ --force-reinstall \
+ -y \
+ -n base \
+ -c defaults conda
```
---
diff --git a/docs/installation/INSTALL_WINDOWS.md b/docs/installation/INSTALL_WINDOWS.md
index 343fcde9a4..c7dc9065ea 100644
--- a/docs/installation/INSTALL_WINDOWS.md
+++ b/docs/installation/INSTALL_WINDOWS.md
@@ -39,7 +39,7 @@ in the wiki
4. Run the command:
- ```bash
+ ```batch
git clone https://github.com/invoke-ai/InvokeAI.git
```
@@ -48,16 +48,20 @@ in the wiki
5. Enter the newly-created InvokeAI folder. From this step forward make sure that you are working in the InvokeAI directory!
-```
-cd InvokeAI
-```
+ ```batch
+ cd InvokeAI
+ ```
6. Run the following two commands:
-```
-conda env create (step 6a)
-conda activate invokeai (step 6b)
-```
+ ```batch title="step 6a"
+ conda env create
+ ```
+
+ ```batch title="step 6b"
+ conda activate invokeai
+ ```
+
This will install all python requirements and activate the "invokeai" environment
which sets PATH and other environment variables properly.
@@ -67,7 +71,7 @@ conda activate invokeai (step 6b)
7. Run the command:
- ```bash
+ ```batch
python scripts\preload_models.py
```
@@ -79,45 +83,44 @@ conda activate invokeai (step 6b)
8. Now you need to install the weights for the big stable diffusion model.
-- For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
-- Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
-- You may be asked to sign a license agreement at this point.
-- Click on "Files and versions" near the top of the page, and then click on the file named `sd-v1-4.ckpt`. You'll be taken to a page that
- prompts you to click the "download" link. Now save the file somewhere safe on your local machine.
-- The weight file is >4 GB in size, so
- downloading may take a while.
+ 1. For running with the released weights, you will first need to set up an acount with Hugging Face (https://huggingface.co).
+ 2. Use your credentials to log in, and then point your browser at https://huggingface.co/CompVis/stable-diffusion-v-1-4-original.
+ 3. You may be asked to sign a license agreement at this point.
+ 4. Click on "Files and versions" near the top of the page, and then click on the file named `sd-v1-4.ckpt`. You'll be taken to a page that
+ prompts you to click the "download" link. Now save the file somewhere safe on your local machine.
+ 5. The weight file is >4 GB in size, so
+ downloading may take a while.
-Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
+ Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
-```
-mkdir -p models\ldm\stable-diffusion-v1
-copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
-```
+ ```batch
+ mkdir -p models\ldm\stable-diffusion-v1
+ copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
+ ```
-Please replace `C:\path\to\sd-v1.4.ckpt` with the correct path to wherever you stashed this file. If you prefer not to copy or move the .ckpt file,
-you may instead create a shortcut to it from within `models\ldm\stable-diffusion-v1\`.
+ Please replace `C:\path\to\sd-v1.4.ckpt` with the correct path to wherever you stashed this file. If you prefer not to copy or move the .ckpt file,
+ you may instead create a shortcut to it from within `models\ldm\stable-diffusion-v1\`.
9. Start generating images!
- ```bash
- # for the pre-release weights
+ ```batch title="for the pre-release weights"
python scripts\invoke.py -l
+ ```
- # for the post-release weights
+ ```batch title="for the post-release weights"
python scripts\invoke.py
```
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the InvokeAI directory (step 5, `cd \path\to\InvokeAI`), run `conda activate invokeai` (step 6b), and then launch the invoke script (step 9).
- **Note:** Tildebyte has written an alternative
+!!! tip "Tildebyte has written an alternative"
+
["Easy peasy Windows install"](https://github.com/invoke-ai/InvokeAI/wiki/Easy-peasy-Windows-install)
which uses the Windows Powershell and pew. If you are having trouble with
Anaconda on Windows, give this a try (or try it first!)
---
-This distribution is changing rapidly. If you used the `git clone` method (step 5) to download the InvokeAI directory, then to update to the latest and greatest version, launch the Anaconda window, enter `InvokeAI`, and type:
-
This distribution is changing rapidly. If you used the `git clone` method
(step 5) to download the stable-diffusion directory, then to update to the
latest and greatest version, launch the Anaconda window, enter
diff --git a/mkdocs.yml b/mkdocs.yml
index 209372562e..86713729d5 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,12 +1,12 @@
# General
-site_name: Dream Script Docs
-site_url: https://invoke-ai.github.io/InvokeAI/
+site_name: Stable Diffusion Toolkit Docs
+site_url: https://invoke-ai.github.io/InvokeAI
site_author: mauwii
dev_addr: "127.0.0.1:8080"
# Repository
-repo_name: invoke-ai/InvokeAI
-repo_url: https://invoke-ai.github.io/InvokeAI/
+repo_name: 'invoke-ai/InvokeAI'
+repo_url: 'https://github.com/invoke-ai/InvokeAI'
edit_uri: edit/main/docs/
# Copyright
Formally known as lstein/stable-diffusion  @@ -29,8 +28,8 @@ template: main.html [CI checks on dev link]: https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Adevelopment [CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github [CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml -[discord badge]: https://flat.badgen.net/discord/members/htRgbc7e?icon=discord -[discord link]: https://discord.com/invite/htRgbc7e +[discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord +[discord link]: https://discord.gg/ZmtBAhwWhy [github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github [github forks link]: https://useful-forks.github.io/?repo=lstein%2Fstable-diffusion [github open issues badge]: https://flat.badgen.net/github/open-issues/invoke-ai/InvokeAI?icon=github @@ -53,14 +52,13 @@ various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. -Quick links: -
-
-
- Downloads and Source Code -
- Documentation and Tutorials -
- Discord Server -
- Bug Reports -
- Discussions, suggestions and Q&A -