mirror of
https://github.com/invoke-ai/InvokeAI
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Bring main back into a consistent state with other branches
- Due to misuse of rebase command, main was transiently in an inconsistent state. - This repairs the damage, and adds a few post-release patches that ensure stable conda installs on Mac and Windows.
This commit is contained in:
Lincoln Stein
267
docs/installation/INSTALLING_MODELS.md
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267
docs/installation/INSTALLING_MODELS.md
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@ -0,0 +1,267 @@
|
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---
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title: Installing Models
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---
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# :octicons-paintbrush-16: Installing Models
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|
||||
## Model Weight Files
|
||||
|
||||
The model weight files ('*.ckpt') are the Stable Diffusion "secret
|
||||
sauce". They are the product of training the AI on millions of
|
||||
captioned images gathered from multiple sources.
|
||||
|
||||
Originally there was only a single Stable Diffusion weights file,
|
||||
which many people named `model.ckpt`. Now there are dozens or more
|
||||
that have been "fine tuned" to provide particulary styles, genres, or
|
||||
other features. InvokeAI allows you to install and run multiple model
|
||||
weight files and switch between them quickly in the command-line and
|
||||
web interfaces.
|
||||
|
||||
This manual will guide you through installing and configuring model
|
||||
weight files.
|
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|
||||
## Base Models
|
||||
|
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InvokeAI comes with support for a good initial set of models listed in
|
||||
the model configuration file `configs/models.yaml`. They are:
|
||||
|
||||
| Model | Weight File | Description | DOWNLOAD FROM |
|
||||
| ---------------------- | ----------------------------- |--------------------------------- | ----------------|
|
||||
| stable-diffusion-1.5 | v1-5-pruned-emaonly.ckpt | Most recent version of base Stable Diffusion model| https://huggingface.co/runwayml/stable-diffusion-v1-5 |
|
||||
| stable-diffusion-1.4 | sd-v1-4.ckpt | Previous version of base Stable Diffusion model | https://huggingface.co/CompVis/stable-diffusion-v-1-4-original |
|
||||
| inpainting-1.5 | sd-v1-5-inpainting.ckpt | Stable Diffusion 1.5 model specialized for inpainting | https://huggingface.co/runwayml/stable-diffusion-inpainting |
|
||||
| waifu-diffusion-1.3 | model-epoch09-float32.ckpt | Stable Diffusion 1.4 trained to produce anime images | https://huggingface.co/hakurei/waifu-diffusion-v1-3 |
|
||||
| <all models> | vae-ft-mse-840000-ema-pruned.ckpt | A fine-tune file add-on file that improves face generation | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/ |
|
||||
|
||||
|
||||
Note that these files are covered by an "Ethical AI" license which
|
||||
forbids certain uses. You will need to create an account on the
|
||||
Hugging Face website and accept the license terms before you can
|
||||
access the files.
|
||||
|
||||
The predefined configuration file for InvokeAI (located at
|
||||
`configs/models.yaml`) provides entries for each of these weights
|
||||
files. `stable-diffusion-1.5` is the default model used, and we
|
||||
strongly recommend that you install this weights file if nothing else.
|
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|
||||
## Community-Contributed Models
|
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|
||||
There are too many to list here and more are being contributed every
|
||||
day. Hugging Face maintains a [fast-growing
|
||||
repository](https://huggingface.co/sd-concepts-library) of fine-tune
|
||||
(".bin") models that can be imported into InvokeAI by passing the
|
||||
`--embedding_path` option to the `invoke.py` command.
|
||||
|
||||
[This page](https://rentry.org/sdmodels) hosts a large list of
|
||||
official and unofficial Stable Diffusion models and where they can be
|
||||
obtained.
|
||||
|
||||
## Installation
|
||||
|
||||
There are three ways to install weights files:
|
||||
|
||||
1. During InvokeAI installation, the `preload_models.py` script can
|
||||
download them for you.
|
||||
|
||||
2. You can use the command-line interface (CLI) to import, configure
|
||||
and modify new models files.
|
||||
|
||||
3. You can download the files manually and add the appropriate entries
|
||||
to `models.yaml`.
|
||||
|
||||
### Installation via `preload_models.py`
|
||||
|
||||
This is the most automatic way. Run `scripts/preload_models.py` from
|
||||
the console. It will ask you to select which models to download and
|
||||
lead you through the steps of setting up a Hugging Face account if you
|
||||
haven't done so already.
|
||||
|
||||
To start, from within the InvokeAI directory run the command `python
|
||||
scripts/preload_models.py` (Linux/MacOS) or `python
|
||||
scripts\preload_models.py` (Windows):
|
||||
|
||||
```
|
||||
Loading Python libraries...
|
||||
|
||||
** INTRODUCTION **
|
||||
Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
|
||||
and other large models that are needed for text to image generation. At any point you may interrupt
|
||||
this program and resume later.
|
||||
|
||||
** WEIGHT SELECTION **
|
||||
Would you like to download the Stable Diffusion model weights now? [y]
|
||||
|
||||
Choose the weight file(s) you wish to download. Before downloading you
|
||||
will be given the option to view and change your selections.
|
||||
|
||||
[1] stable-diffusion-1.5:
|
||||
The newest Stable Diffusion version 1.5 weight file (4.27 GB) (recommended)
|
||||
Download? [y]
|
||||
[2] inpainting-1.5:
|
||||
RunwayML SD 1.5 model optimized for inpainting (4.27 GB) (recommended)
|
||||
Download? [y]
|
||||
[3] stable-diffusion-1.4:
|
||||
The original Stable Diffusion version 1.4 weight file (4.27 GB)
|
||||
Download? [n] n
|
||||
[4] waifu-diffusion-1.3:
|
||||
Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
|
||||
Download? [n] y
|
||||
[5] ft-mse-improved-autoencoder-840000:
|
||||
StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)
|
||||
Download? [y] y
|
||||
The following weight files will be downloaded:
|
||||
[1] stable-diffusion-1.5*
|
||||
[2] inpainting-1.5
|
||||
[4] waifu-diffusion-1.3
|
||||
[5] ft-mse-improved-autoencoder-840000
|
||||
*default
|
||||
Ok to download? [y]
|
||||
** LICENSE AGREEMENT FOR WEIGHT FILES **
|
||||
|
||||
1. To download the Stable Diffusion weight files you need to read and accept the
|
||||
CreativeML Responsible AI license. If you have not already done so, please
|
||||
create an account using the "Sign Up" button:
|
||||
|
||||
https://huggingface.co
|
||||
|
||||
You will need to verify your email address as part of the HuggingFace
|
||||
registration process.
|
||||
|
||||
2. After creating the account, login under your account and accept
|
||||
the license terms located here:
|
||||
|
||||
https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
|
||||
|
||||
Press <enter> when you are ready to continue:
|
||||
...
|
||||
```
|
||||
|
||||
When the script is complete, you will find the downloaded weights
|
||||
files in `models/ldm/stable-diffusion-v1` and a matching configuration
|
||||
file in `configs/models.yaml`.
|
||||
|
||||
You can run the script again to add any models you didn't select the
|
||||
first time. Note that as a safety measure the script will _never_
|
||||
remove a previously-installed weights file. You will have to do this
|
||||
manually.
|
||||
|
||||
### Installation via the CLI
|
||||
|
||||
You can install a new model, including any of the community-supported
|
||||
ones, via the command-line client's `!import_model` command.
|
||||
|
||||
1. First download the desired model weights file and place it under `models/ldm/stable-diffusion-v1/`.
|
||||
You may rename the weights file to something more memorable if you wish. Record the path of the
|
||||
weights file (e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`)
|
||||
|
||||
2. Launch the `invoke.py` CLI with `python scripts/invoke.py`.
|
||||
|
||||
3. At the `invoke>` command-line, enter the command `!import_model <path to model>`.
|
||||
For example:
|
||||
|
||||
`invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
|
||||
|
||||
(Hint - the CLI supports file path autocompletion. Type a bit of the path
|
||||
name and hit <tab> in order to get a choice of possible completions.)
|
||||
|
||||
4. Follow the wizard's instructions to complete installation as shown in the example
|
||||
here:
|
||||
|
||||
```
|
||||
invoke> <b>!import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt</b>
|
||||
>> Model import in process. Please enter the values needed to configure this model:
|
||||
|
||||
Name for this model: <b>arabian-nights</b>
|
||||
Description of this model: <b>Arabian Nights Fine Tune v1.0</b>
|
||||
Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
|
||||
Default image width: <b>512</b>
|
||||
Default image height: <b>512</b>
|
||||
>> New configuration:
|
||||
arabian-nights:
|
||||
config: configs/stable-diffusion/v1-inference.yaml
|
||||
description: Arabian Nights Fine Tune v1.0
|
||||
height: 512
|
||||
weights: models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
|
||||
width: 512
|
||||
OK to import [n]? <b>y</b>
|
||||
>> Caching model stable-diffusion-1.4 in system RAM
|
||||
>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/arabian-nights-1.0.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
|
||||
|
||||
```
|
||||
|
||||
If you've previously installed the fine-tune VAE file `vae-ft-mse-840000-ema-pruned.ckpt`,
|
||||
the wizard will also ask you if you want to add this VAE to the model.
|
||||
|
||||
The appropriate entry for this model will be added to `configs/models.yaml` and it will
|
||||
be available to use in the CLI immediately.
|
||||
|
||||
The CLI has additional commands for switching among, viewing, editing,
|
||||
deleting the available models. These are described in [Command Line
|
||||
Client](../features/CLI.md#model-selection-and-importation), but the two most
|
||||
frequently-used are `!models` and `!switch <name of model>`. The first
|
||||
prints a table of models that InvokeAI knows about and their load
|
||||
status. The second will load the requested model and lets you switch
|
||||
back and forth quickly among loaded models.
|
||||
|
||||
### Manually editing of `configs/models.yaml`
|
||||
|
||||
If you are comfortable with a text editor then you may simply edit
|
||||
`models.yaml` directly.
|
||||
|
||||
First you need to download the desired .ckpt file and place it in
|
||||
`models/ldm/stable-diffusion-v1` as descirbed in step #1 in the
|
||||
previous section. Record the path to the weights file,
|
||||
e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
|
||||
|
||||
Then using a **text** editor (e.g. the Windows Notepad application),
|
||||
open the file `configs/models.yaml`, and add a new stanza that follows
|
||||
this model:
|
||||
|
||||
```
|
||||
arabian-nights-1.0:
|
||||
description: A great fine-tune in Arabian Nights style
|
||||
weights: ./models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
|
||||
config: ./configs/stable-diffusion/v1-inference.yaml
|
||||
width: 512
|
||||
height: 512
|
||||
vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
|
||||
default: false
|
||||
```
|
||||
|
||||
* arabian-nights-1.0
|
||||
- This is the name of the model that you will refer to from within the
|
||||
CLI and the WebGUI when you need to load and use the model.
|
||||
|
||||
* description
|
||||
- Any description that you want to add to the model to remind you what
|
||||
it is.
|
||||
|
||||
* weights
|
||||
- Relative path to the .ckpt weights file for this model.
|
||||
|
||||
* config
|
||||
- This is the confusingly-named configuration file for the model itself.
|
||||
Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens
|
||||
to need a custom configuration, in which case the place you downloaded it
|
||||
from will tell you what to use instead. For example, the runwayML custom
|
||||
inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`.
|
||||
This is already inclued in the InvokeAI distribution and is configured automatically
|
||||
for you by the `preload_models.py` script.
|
||||
|
||||
* vae
|
||||
- If you want to add a VAE file to the model, then enter its path here.
|
||||
|
||||
* width, height
|
||||
- This is the width and height of the images used to train the model.
|
||||
Currently they are always 512 and 512.
|
||||
|
||||
Save the `models.yaml` and relaunch InvokeAI. The new model should now be
|
||||
available for your use.
|
||||
|
||||
|
||||
@ -36,20 +36,6 @@ another environment with NVIDIA GPUs on-premises or in the cloud.
|
||||
|
||||
### Prerequisites
|
||||
|
||||
#### Get the data files
|
||||
|
||||
Go to
|
||||
[Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original),
|
||||
and click "Access repository" to Download the model file `sd-v1-4.ckpt` (~4 GB)
|
||||
to `~/Downloads`. You'll need to create an account but it's quick and free.
|
||||
|
||||
Also download the face restoration model.
|
||||
|
||||
```Shell
|
||||
cd ~/Downloads
|
||||
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth
|
||||
```
|
||||
|
||||
#### Install [Docker](https://github.com/santisbon/guides#docker)
|
||||
|
||||
On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the
|
||||
@ -57,86 +43,61 @@ 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.
|
||||
|
||||
#### Get a Huggingface-Token
|
||||
|
||||
Go to [Hugging Face](https://huggingface.co/settings/tokens), create a token and
|
||||
temporary place it somewhere like a open texteditor window (but dont save it!,
|
||||
only keep it open, we need it in the next step)
|
||||
|
||||
### Setup
|
||||
|
||||
Set the fork you want to use and other variables.
|
||||
|
||||
```Shell
|
||||
TAG_STABLE_DIFFUSION="santisbon/stable-diffusion"
|
||||
PLATFORM="linux/arm64"
|
||||
GITHUB_STABLE_DIFFUSION="-b orig-gfpgan https://github.com/santisbon/stable-diffusion.git"
|
||||
REQS_STABLE_DIFFUSION="requirements-linux-arm64.txt"
|
||||
CONDA_SUBDIR="osx-arm64"
|
||||
!!! tip
|
||||
|
||||
echo $TAG_STABLE_DIFFUSION
|
||||
echo $PLATFORM
|
||||
echo $GITHUB_STABLE_DIFFUSION
|
||||
echo $REQS_STABLE_DIFFUSION
|
||||
echo $CONDA_SUBDIR
|
||||
I preffer to save my env vars
|
||||
in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
|
||||
them when I come back.
|
||||
|
||||
The build- and run- scripts contain default values for almost everything,
|
||||
besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
|
||||
created in the last step.
|
||||
|
||||
Some Suggestions of variables you may want to change besides the Token:
|
||||
|
||||
| Environment-Variable | Description |
|
||||
| ------------------------------------------------------------------- | ------------------------------------------------------------------------ |
|
||||
| `HUGGINGFACE_TOKEN="hg_aewirhghlawrgkjbarug2"` | This is the only required variable, without you can't get the checkpoint |
|
||||
| `ARCH=aarch64` | if you are using a ARM based CPU |
|
||||
| `INVOKEAI_TAG=yourname/invokeai:latest` | the Container Repository / Tag which will be used |
|
||||
| `INVOKEAI_CONDA_ENV_FILE=environment-linux-aarch64.yml` | since environment.yml wouldn't work with aarch |
|
||||
| `INVOKEAI_GIT="-b branchname https://github.com/username/reponame"` | if you want to use your own fork |
|
||||
|
||||
#### Build the Image
|
||||
|
||||
I provided a build script, which is located in `docker-build/build.sh` but still
|
||||
needs to be executed from the Repository root.
|
||||
|
||||
```bash
|
||||
docker-build/build.sh
|
||||
```
|
||||
|
||||
Create a Docker volume for the downloaded model files.
|
||||
The build Script not only builds the container, but also creates the docker
|
||||
volume if not existing yet, or if empty it will just download the models. When
|
||||
it is done you can run the container via the run script
|
||||
|
||||
```Shell
|
||||
docker volume create my-vol
|
||||
```bash
|
||||
docker-build/run.sh
|
||||
```
|
||||
|
||||
Copy the data files to the Docker volume using a lightweight Linux container.
|
||||
We'll need the models at run time. You just need to create the container with
|
||||
the mountpoint; no need to run this dummy container.
|
||||
When used without arguments, the container will start the website and provide
|
||||
you the link to open it. But if you want to use some other parameters you can
|
||||
also do so.
|
||||
|
||||
```Shell
|
||||
cd ~/Downloads # or wherever you saved the files
|
||||
!!! warning "Deprecated"
|
||||
|
||||
docker create --platform $PLATFORM --name dummy --mount source=my-vol,target=/data alpine
|
||||
|
||||
docker cp sd-v1-4.ckpt dummy:/data
|
||||
docker cp GFPGANv1.4.pth dummy:/data
|
||||
```
|
||||
|
||||
Get the repo and download the Miniconda installer (we'll need it at build time).
|
||||
Replace the URL with the version matching your container OS and the architecture
|
||||
it will run on.
|
||||
|
||||
```Shell
|
||||
cd ~
|
||||
git clone $GITHUB_STABLE_DIFFUSION
|
||||
|
||||
cd stable-diffusion/docker-build
|
||||
chmod +x entrypoint.sh
|
||||
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh && chmod +x anaconda.sh
|
||||
```
|
||||
|
||||
Build the Docker image. Give it any tag `-t` that you want.
|
||||
Choose the Linux container's host platform: x86-64/Intel is `amd64`. Apple
|
||||
silicon is `arm64`. If deploying the container to the cloud to leverage powerful
|
||||
GPU instances you'll be on amd64 hardware but if you're just trying this out
|
||||
locally on Apple silicon choose arm64.
|
||||
The application uses libraries that need to match the host environment so use
|
||||
the appropriate requirements file.
|
||||
Tip: Check that your shell session has the env variables set above.
|
||||
|
||||
```Shell
|
||||
docker build -t $TAG_STABLE_DIFFUSION \
|
||||
--platform $PLATFORM \
|
||||
--build-arg gsd=$GITHUB_STABLE_DIFFUSION \
|
||||
--build-arg rsd=$REQS_STABLE_DIFFUSION \
|
||||
--build-arg cs=$CONDA_SUBDIR \
|
||||
.
|
||||
```
|
||||
|
||||
Run a container using your built image.
|
||||
Tip: Make sure you've created and populated the Docker volume (above).
|
||||
|
||||
```Shell
|
||||
docker run -it \
|
||||
--rm \
|
||||
--platform $PLATFORM \
|
||||
--name stable-diffusion \
|
||||
--hostname stable-diffusion \
|
||||
--mount source=my-vol,target=/data \
|
||||
$TAG_STABLE_DIFFUSION
|
||||
```
|
||||
From here on it is the rest of the previous Docker-Docs, which will still
|
||||
provide usefull informations for one or the other.
|
||||
|
||||
## Usage (time to have fun)
|
||||
|
||||
@ -240,7 +201,8 @@ server with:
|
||||
python3 scripts/invoke.py --full_precision --web
|
||||
```
|
||||
|
||||
If it's running on your Mac point your Mac web browser to http://127.0.0.1:9090
|
||||
If it's running on your Mac point your Mac web browser to
|
||||
<http://127.0.0.1:9090>
|
||||
|
||||
Press Control-C at the command line to stop the web server.
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
---
|
||||
title: Linux
|
||||
title: Manual Installation, Linux
|
||||
---
|
||||
|
||||
# :fontawesome-brands-linux: Linux
|
||||
@ -43,6 +43,7 @@ title: Linux
|
||||
environment named `invokeai` and activate the environment.
|
||||
|
||||
```bash
|
||||
(base) rm -rf src # (this is a precaution in case there is already a src directory)
|
||||
(base) ~/InvokeAI$ conda env create
|
||||
(base) ~/InvokeAI$ conda activate invokeai
|
||||
(invokeai) ~/InvokeAI$
|
||||
@ -51,58 +52,54 @@ title: Linux
|
||||
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:
|
||||
6. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
|
||||
|
||||
```bash
|
||||
(invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
|
||||
```
|
||||
|
||||
!!! note
|
||||
This script will lead you through the process of creating an account on Hugging Face,
|
||||
accepting the terms and conditions of the Stable Diffusion model license, and
|
||||
obtaining an access token for downloading. It will then download and install the
|
||||
weights files for you.
|
||||
|
||||
This step is necessary because I modified the original just-in-time
|
||||
model loading scheme to allow the script to work on GPU machines that are not
|
||||
internet connected. See [Preload Models](../features/OTHER.md#preload-models)
|
||||
Please see [../features/INSTALLING_MODELS.md] for a manual process for doing the
|
||||
same thing.
|
||||
|
||||
7. Now you need to install the weights for the stable diffusion model.
|
||||
7. Start generating images!
|
||||
|
||||
- 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 [here](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. Save the file somewhere safe on your local machine.
|
||||
# Command-line interface
|
||||
(invokeai) python scripts/invoke.py
|
||||
|
||||
Now run the following commands from within the stable-diffusion directory.
|
||||
This will create a symbolic link from the stable-diffusion model.ckpt file, to
|
||||
the true location of the `sd-v1-4.ckpt` file.
|
||||
# or run the web interface on localhost:9090!
|
||||
(invokeai) python scripts/invoke.py --web
|
||||
|
||||
```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
|
||||
```
|
||||
# or run the web interface on your machine's network interface!
|
||||
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
|
||||
|
||||
8. Start generating images!
|
||||
To use an alternative model you may invoke the `!switch` command in
|
||||
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
|
||||
either the CLI or the Web UI. See [Command Line
|
||||
Client](../features/CLI.md#model-selection-and-importation). The
|
||||
model names are defined in `configs/models.yaml`.
|
||||
|
||||
```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 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.
|
||||
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:
|
||||
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$ rm -rf src # prevents conda freezing errors
|
||||
(invokeai) ~/InvokeAI$ conda env update -f environment.yml
|
||||
```
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
---
|
||||
title: macOS
|
||||
title: Manual Installation, macOS
|
||||
---
|
||||
|
||||
# :fontawesome-brands-apple: macOS
|
||||
@ -19,18 +19,9 @@ an issue on Github and we will do our best to help.
|
||||
|
||||
## Installation
|
||||
|
||||
First you need to download a large checkpoint file.
|
||||
|
||||
1. Sign up at https://huggingface.co
|
||||
2. Go to the [Stable diffusion diffusion model page](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
|
||||
3. Accept the terms and click Access Repository
|
||||
4. Download [sd-v1-4.ckpt (4.27 GB)](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/blob/main/sd-v1-4.ckpt) and note where you have saved it (probably the Downloads folder). You may want to move it somewhere else for longer term storage - SD needs this file to run.
|
||||
|
||||
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).
|
||||
|
||||
!!! todo "Homebrew"
|
||||
|
||||
If you have no brew installation yet (otherwise skip):
|
||||
First you will install the "brew" package manager. Skip this if brew is already installed.
|
||||
|
||||
```bash title="install brew (and Xcode command line tools)"
|
||||
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
|
||||
@ -94,25 +85,6 @@ While that is downloading, open Terminal and run the following commands one at a
|
||||
cd InvokeAI
|
||||
```
|
||||
|
||||
!!! todo "Wait until the checkpoint-file download finished, then proceed"
|
||||
|
||||
We will leave the big checkpoint wherever you stashed it for long-term storage,
|
||||
and make a link to it from the repo's folder. This allows you to use it for
|
||||
other repos, or if you need to delete Invoke AI, you won't have to download it again.
|
||||
|
||||
```{.bash .annotate}
|
||||
# Make the directory in the repo for the symlink
|
||||
mkdir -p models/ldm/stable-diffusion-v1/
|
||||
|
||||
# This is the folder where you put the checkpoint file `sd-v1-4.ckpt`
|
||||
PATH_TO_CKPT="$HOME/Downloads" # (1)!
|
||||
|
||||
# Create a link to the checkpoint
|
||||
ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
|
||||
```
|
||||
|
||||
1. replace `$HOME/Downloads` with the Location where you actually stored the Checkppoint (`sd-v1-4.ckpt`)
|
||||
|
||||
!!! todo "Create the environment & install packages"
|
||||
|
||||
=== "M1 Mac"
|
||||
@ -131,25 +103,40 @@ While that is downloading, open Terminal and run the following commands one at a
|
||||
# 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
|
||||
(invokeai) python scripts/preload_models.py
|
||||
|
||||
# Run SD!
|
||||
(invokeai) python scripts/dream.py
|
||||
|
||||
# or run the web interface!
|
||||
(invokeai) python scripts/invoke.py --web
|
||||
|
||||
# The original scripts should work as well.
|
||||
(invokeai) 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.
|
||||
it isn't required but won't hurt.
|
||||
|
||||
!!! todo "Download the model weight files"
|
||||
|
||||
The `preload_models.py` script downloads and installs the model weight
|
||||
files for you. It will lead you through the process of getting a Hugging Face
|
||||
account, accepting the Stable Diffusion model weight license agreement, and
|
||||
creating a download token:
|
||||
|
||||
# This will take some time, depending on the speed of your internet connection
|
||||
# and will consume about 10GB of space
|
||||
(invokeai) python scripts/preload_models.py
|
||||
|
||||
!! todo "Run InvokeAI!"
|
||||
|
||||
# Command-line interface
|
||||
(invokeai) python scripts/invoke.py
|
||||
|
||||
# or run the web interface on localhost:9090!
|
||||
(invokeai) python scripts/invoke.py --web
|
||||
|
||||
# or run the web interface on your machine's network interface!
|
||||
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
|
||||
|
||||
To use an alternative model you may invoke the `!switch` command in
|
||||
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
|
||||
either the CLI or the Web UI. See [Command Line
|
||||
Client](../features/CLI.md#model-selection-and-importation). The
|
||||
model names are defined in `configs/models.yaml`.
|
||||
|
||||
---
|
||||
|
||||
## Common problems
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
---
|
||||
title: Windows
|
||||
title: Manual Installation, Windows
|
||||
---
|
||||
|
||||
# :fontawesome-brands-windows: Windows
|
||||
@ -69,49 +69,42 @@ in the wiki
|
||||
environment file isn't specified, conda will default to `environment.yml`. You will need
|
||||
to provide the `-f` option if you wish to load a different environment file at any point.
|
||||
|
||||
7. Run the command:
|
||||
7. Load the big stable diffusion weights files and a couple of smaller machine-learning models:
|
||||
|
||||
```batch
|
||||
python scripts\preload_models.py
|
||||
```bash
|
||||
(invokeai) ~/InvokeAI$ python3 scripts/preload_models.py
|
||||
```
|
||||
|
||||
This installs several machine learning models that stable diffusion requires.
|
||||
!!! note
|
||||
This script will lead you through the process of creating an account on Hugging Face,
|
||||
accepting the terms and conditions of the Stable Diffusion model license, and
|
||||
obtaining an access token for downloading. It will then download and install the
|
||||
weights files for you.
|
||||
|
||||
Note: This step is required. This was done because some users may might be
|
||||
blocked by firewalls or have limited internet connectivity for the models to
|
||||
be downloaded just-in-time.
|
||||
Please see [../features/INSTALLING_MODELS.md] for a manual process for doing the
|
||||
same thing.
|
||||
|
||||
8. Now you need to install the weights for the big stable diffusion model.
|
||||
8. Start generating images!
|
||||
|
||||
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.
|
||||
# Command-line interface
|
||||
(invokeai) python scripts/invoke.py
|
||||
|
||||
Now run the following commands from **within the InvokeAI directory** to copy the weights file to the right place:
|
||||
# or run the web interface on localhost:9090!
|
||||
(invokeai) python scripts/invoke.py --web
|
||||
|
||||
```batch
|
||||
mkdir -p models\ldm\stable-diffusion-v1
|
||||
copy C:\path\to\sd-v1-4.ckpt models\ldm\stable-diffusion-v1\model.ckpt
|
||||
```
|
||||
# or run the web interface on your machine's network interface!
|
||||
(invokeai) python scripts/invoke.py --web --host 0.0.0.0
|
||||
|
||||
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\`.
|
||||
To use an alternative model you may invoke the `!switch` command in
|
||||
the CLI, or pass `--model <model_name>` during `invoke.py` launch for
|
||||
either the CLI or the Web UI. See [Command Line
|
||||
Client](../features/CLI.md#model-selection-and-importation). The
|
||||
model names are defined in `configs/models.yaml`.
|
||||
|
||||
9. Start generating images!
|
||||
|
||||
```batch title="for the pre-release weights"
|
||||
python scripts\invoke.py -l
|
||||
```
|
||||
|
||||
```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).
|
||||
9. 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).
|
||||
|
||||
!!! tip "Tildebyte has written an alternative"
|
||||
|
||||
|
||||
Reference in New Issue
Block a user