Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Create README.md

Browse Source
This commit is contained in:
Lincoln Stein 2022-08-19 15:33:18 -04:00 committed by GitHub
parent ce5b94bf40
commit ae821ce0e6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 77 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

80
README.md
View File

@ -19,8 +19,9 @@ from the command-line interface is very fast.
The script uses the readline library to allow for in-line editing,
command history (up and down arrows), autocompletion, and more.
Note that this has only been tested in the Linux environment. Testing
and tweaking for Windows is in progress.
The script is confirmed to work on Linux and Windows systems. It should
work on MacOSX as well, but this is not confirmed. Note that this script
runs from the command-line (CMD or Terminal window), and does not have a GUI.
~~~~
(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py
@ -45,7 +46,7 @@ dream> "there's a fly in my soup" -n6 -g
seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
~~~~
The dream> prompt's arguments are pretty much
The dream> prompt's arguments are pretty much
identical to those used in the Discord bot, except you don't need to
type "!dream" (it doesn't hurt if you do). A significant change is that creation of individual images
is now the default
@ -76,6 +77,79 @@ completely). The default is 0.75, and ranges from 0.25-0.75 give interesting res
### Linux/Mac
1. You will need to install the following prerequisites if they are not already available. Use your
operating system's preferred installer
* Python (version 3.8 or higher)
* git
2. Install the Python Anaconda environment manager using pip3.
```
~$ pip3 install anaconda
```
After installing anaconda, you should log out of your system and log back in. If the installation
worked, your command prompt will be prefixed by the name of the current anaconda environment, "(base)".
3. Copy the stable-diffusion source code from GitHub:
```
(base) ~$ git clone https://github.com/lstein/stable-diffusion.git
```
This will create stable-diffusion folder where you will follow the rest of the steps.
6. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
```
(base) ~$ cd stable-diffusion
(base) ~/stable-diffusion$
```
7. Use anaconda to copy necessary python packages, create a new python environment named "ldm",
and activate the environment.
```
(base) ~/stable-diffusion$ conda env create -f environment.yaml
(base) ~/stable-diffusion$ conda activate ldm
(ldm) ~/stable-diffusion$
```
After these steps, your command prompt will be prefixed by "(ldm)" as shown above.
8. Load a couple of small machine-learning models required by stable diffusion:
```
(ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
```
9. Now you need to install the weights for the released stable diffusion model.
For testing prior to the release of the real weights, you can use an older weight file that produces low-quality images. Create a directory within stable-diffusion named "models/ldm/text2img.large", and use the wget URL downloader tool to copy the weight file into it:
```
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/text2img-large
(ldm) ~/stable-diffusion$ wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt
```
For testing with the released weighs, you will do something similar, but with a directory named "models/ldm/stable-diffusion-v1"
```
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
(ldm) ~/stable-diffusion$ wget -O models/ldm/stable-diffusion-v1/model.ckpt <ENTER URL HERE>
```
These weight files are ~5 GB in size, so downloading may take a while.
10. Start generating images!
```
# for the pre-release weights use the **-l** switch
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -l
# for the post-release weights do not use the switch
(ldm) ~/stable-diffusion$ python3 scripts/dream.py
# for additional configuration switches and arguments, use **-h**
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -h
```
11. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 7, second command), enter the "stable-diffusion"
directory, and then launch the dream script (step 10).
### Updating to newer versions of the script
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 "stable-diffusion", and type:
```
(ldm) ~/stable-diffusion$ git pull
```
This will bring your local copy into sync with the remote one.
For installation, follow the instructions from the original CompViz/stable-diffusion
README which is appended to this README for your convenience. A few things to be aware of: