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@ -150,30 +150,6 @@ This distribution is changing rapidly. If you used the "git clone" method (step
```
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:
1. You will need the stable-diffusion model weights, which have to be downloaded separately as described
in the CompViz instructions. They are expected to be released in the latter half of August.
2. If you do not have the weights and want to play with low-quality image generation, then you can use
the public LAION400m weights, which can be installed like this:
~~~~
mkdir -p models/ldm/text2img-large/
wget -O models/ldm/text2img-large/model.ckpt https://ommer-lab.com/files/latent-diffusion/nitro/txt2img-f8-large/model.ckpt
~~~~
You will then have to invoke dream.py with the --laion400m (or -l for short) flag:
~~~~
(ldm) ~/stable-diffusion$ python3 ./scripts/dream.py -l
~~~~
3. To get around issues that arise when running the stable diffusion model on a machine without internet
connectivity, I wrote a script that pre-downloads internet dependencies. Whether or not your GPU machine
has connectivity, you will need to run this preloading script before the first run of dream.py. See
"Workaround for machines with limited internet connectivity" below for the walkthrough.
### Windows
1. Install the most recent Python from here: https://www.python.org/downloads/windows/
@ -204,7 +180,7 @@ This will install all python requirements and activate the "ldm" environment whi
8. Run the command:
```
python scripts/preload_models.py
python scripts\preload_models.py
```
This installs two machine learning models that stable diffusion requires.