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Docs Update (#466)

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Authored-by: @blessedcoolant 
Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com>
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2022-09-12 03:52:43 +12:00
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# **Linux Installation**
1. You will need to install the following prerequisites if they are not already available. Use your operating system's preferred installer
- Python (version 3.8.5 recommended; higher may work)
- git
2. Install the Python Anaconda environment manager.
```
~$ wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
~$ chmod +x Anaconda3-2022.05-Linux-x86_64.sh
~$ ./Anaconda3-2022.05-Linux-x86_64.sh
```
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.
4. 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$
```
5. 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.
6. Load a couple of small machine-learning models required by stable diffusion:
```
(ldm) ~/stable-diffusion$ python3 scripts/preload_models.py
```
Note that 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)
7. Now you need to install the weights for the 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. Save the file somewhere safe on your local machine.
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.
```
(ldm) ~/stable-diffusion$ mkdir -p models/ldm/stable-diffusion-v1
(ldm) ~/stable-diffusion$ 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
(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 or --help
(ldm) ~/stable-diffusion$ python3 scripts/dream.py -h
```
9. Subsequently, to relaunch the script, be sure to run "conda activate ldm" (step 5, second command), enter the `stable-diffusion` directory, and then launch the dream script (step 8). If you forget to activate the ldm 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 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.

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# **macOS Instructions**
Requirements
- macOS 12.3 Monterey or later
- Python
- Patience
- Apple Silicon\*
\*I haven't tested any of this on Intel Macs but I have read that one person got it to work, so Apple Silicon might not be requried.
Things have moved really fast and so these instructions change often and are often out-of-date. One of the problems is that there are so many different ways to run this.
We are trying to build a testing setup so that when we make changes it doesn't always break.
How to (this hasn't been 100% tested yet):
First get the weights checkpoint download started - it's big:
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)
While that is downloading, open Terminal and run the following commands one at a time.
```
# install brew (and Xcode command line tools):
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
#
# Now there are two different routes to get the Python (miniconda) environment up and running:
# 1. Alongside pyenv
# 2. No pyenv
#
# If you don't know what we are talking about, choose 2.
#
# NOW EITHER DO
# 1. Installing alongside pyenv
brew install pyenv-virtualenv # you might have this from before, no problem
pyenv install anaconda3-latest
pyenv virtualenv anaconda3-latest lstein-stable-diffusion
pyenv activate lstein-stable-diffusion
# OR,
# 2. Installing standalone
# install python 3, git, cmake, protobuf:
brew install cmake protobuf rust
# install miniconda (M1 arm64 version):
curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh -o Miniconda3-latest-MacOSX-arm64.sh
/bin/bash Miniconda3-latest-MacOSX-arm64.sh
# EITHER WAY,
# continue from here
# clone the repo
git clone https://github.com/lstein/stable-diffusion.git
cd stable-diffusion
#
# wait until the checkpoint file has downloaded, then proceed
#
# create symlink to checkpoint
mkdir -p models/ldm/stable-diffusion-v1/
PATH_TO_CKPT="$HOME/Downloads" # or wherever you saved sd-v1-4.ckpt
ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
# install packages
PIP_EXISTS_ACTION=w CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yaml
conda activate ldm
# only need to do this once
python scripts/preload_models.py
# run SD!
python scripts/dream.py --full_precision # half-precision requires autocast and won't work
```
The original scripts should work as well.
```
python scripts/orig_scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
```
Note, `export PIP_EXISTS_ACTION=w` is a precaution to fix `conda env create -f environment-mac.yaml` never finishing in some situations. So it isn't required but wont hurt.
After you follow all the instructions and run dream.py you might get several errors. Here's the errors I've seen and found solutions for.
### Is it slow?
Be sure to specify 1 sample and 1 iteration.
python ./scripts/orig_scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
### Doesn't work anymore?
PyTorch nightly includes support for MPS. Because of this, this setup is inherently unstable. One morning I woke up and it no longer worked no matter what I did until I switched to miniforge. However, I have another Mac that works just fine with Anaconda. If you can't get it to work, please search a little first because many of the errors will get posted and solved. If you can't find a solution please [create an issue](https://github.com/lstein/stable-diffusion/issues).
One debugging step is to update to the latest version of PyTorch nightly.
conda install pytorch torchvision torchaudio -c pytorch-nightly
If `conda env create -f environment-mac.yaml` takes forever run this.
git clean -f
And run this.
conda clean --yes --all
Or you could reset Anaconda.
conda update --force-reinstall -y -n base -c defaults conda
### "No module named cv2", torch, 'ldm', 'transformers', 'taming', etc.
There are several causes of these errors.
First, did you remember to `conda activate ldm`? If your terminal prompt
begins with "(ldm)" then you activated it. If it begins with "(base)"
or something else you haven't.
Second, you might've run `./scripts/preload_models.py` or `./scripts/dream.py`
instead of `python ./scripts/preload_models.py` or `python ./scripts/dream.py`.
The cause of this error is long so it's below.
Third, if it says you're missing taming you need to rebuild your virtual
environment.
conda env remove -n ldm
conda env create -f environment-mac.yaml
Fourth, If you have activated the ldm virtual environment and tried rebuilding it, maybe the problem could be that I have something installed that you don't and you'll just need to manually install it. Make sure you activate the virtual environment so it installs there instead of
globally.
conda activate ldm
pip install *name*
You might also need to install Rust (I mention this again below).
### How many snakes are living in your computer?
Here's the reason why you have to specify which python to use.
There are several versions of python on macOS and the computer is
picking the wrong one. More specifically, preload_models.py and dream.py says to
find the first `python3` in the path environment variable. You can see which one
it is picking with `which python3`. These are the mostly likely paths you'll see.
% which python3
/usr/bin/python3
The above path is part of the OS. However, that path is a stub that asks you if
you want to install Xcode. If you have Xcode installed already,
/usr/bin/python3 will execute /Library/Developer/CommandLineTools/usr/bin/python3 or
/Applications/Xcode.app/Contents/Developer/usr/bin/python3 (depending on which
Xcode you've selected with `xcode-select`).
% which python3
/opt/homebrew/bin/python3
If you installed python3 with Homebrew and you've modified your path to search
for Homebrew binaries before system ones, you'll see the above path.
% which python
/opt/anaconda3/bin/python
If you drop the "3" you get an entirely different python. Note: starting in
macOS 12.3, /usr/bin/python no longer exists (it was python 2 anyway).
If you have Anaconda installed, this is what you'll see. There is a
/opt/anaconda3/bin/python3 also.
(ldm) % which python
/Users/name/miniforge3/envs/ldm/bin/python
This is what you'll see if you have miniforge and you've correctly activated
the ldm environment. This is the goal.
It's all a mess and you should know [how to modify the path environment variable](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac)
if you want to fix it. Here's a brief hint of all the ways you can modify it
(don't really have the time to explain it all here).
- ~/.zshrc
- ~/.bash_profile
- ~/.bashrc
- /etc/paths.d
- /etc/path
Which one you use will depend on what you have installed except putting a file
in /etc/paths.d is what I prefer to do.
### Debugging?
Tired of waiting for your renders to finish before you can see if it
works? Reduce the steps! The image quality will be horrible but at least you'll
get quick feedback.
python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'...
python scripts/preload_models.py
### "The operator [name] is not current implemented for the MPS device." (sic)
Example error.
```
...
NotImplementedError: The operator 'aten::_index_put_impl_' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
```
The lstein branch includes this fix in [environment-mac.yaml](https://github.com/lstein/stable-diffusion/blob/main/environment-mac.yaml).
### "Could not build wheels for tokenizers"
I have not seen this error because I had Rust installed on my computer before I started playing with Stable Diffusion. The fix is to install Rust.
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
### How come `--seed` doesn't work?
First this:
> Completely reproducible results are not guaranteed across PyTorch
> releases, individual commits, or different platforms. Furthermore,
> results may not be reproducible between CPU and GPU executions, even
> when using identical seeds.
[PyTorch docs](https://pytorch.org/docs/stable/notes/randomness.html)
Second, we might have a fix that at least gets a consistent seed sort of. We're
still working on it.
### libiomp5.dylib error?
OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
You are likely using an Intel package by mistake. Be sure to run conda with
the environment variable `CONDA_SUBDIR=osx-arm64`, like so:
`CONDA_SUBDIR=osx-arm64 conda install ...`
This error happens with Anaconda on Macs when the Intel-only `mkl` is pulled in by
a dependency. [nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
is a metapackage designed to prevent this, by making it impossible to install
`mkl`, but if your environment is already broken it may not work.
Do _not_ use `os.environ['KMP_DUPLICATE_LIB_OK']='True'` or equivalents as this
masks the underlying issue of using Intel packages.
### Not enough memory.
This seems to be a common problem and is probably the underlying
problem for a lot of symptoms (listed below). The fix is to lower your
image size or to add `model.half()` right after the model is loaded. I
should probably test it out. I've read that the reason this fixes
problems is because it converts the model from 32-bit to 16-bit and
that leaves more RAM for other things. I have no idea how that would
affect the quality of the images though.
See [this issue](https://github.com/CompVis/stable-diffusion/issues/71).
### "Error: product of dimension sizes > 2\*\*31'"
This error happens with img2img, which I haven't played with too much
yet. But I know it's because your image is too big or the resolution
isn't a multiple of 32x32. Because the stable-diffusion model was
trained on images that were 512 x 512, it's always best to use that
output size (which is the default). However, if you're using that size
and you get the above error, try 256 x 256 or 512 x 256 or something
as the source image.
BTW, 2\*\*31-1 = [2,147,483,647](https://en.wikipedia.org/wiki/2,147,483,647#In_computing), which is also 32-bit signed [LONG_MAX](https://en.wikipedia.org/wiki/C_data_types) in C.
### I just got Rickrolled! Do I have a virus?
You don't have a virus. It's part of the project. Here's
[Rick](https://github.com/lstein/stable-diffusion/blob/main/assets/rick.jpeg)
and here's [the
code](https://github.com/lstein/stable-diffusion/blob/69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc/scripts/txt2img.py#L79)
that swaps him in. It's a NSFW filter, which IMO, doesn't work very
good (and we call this "computer vision", sheesh).
Actually, this could be happening because there's not enough RAM. You could try the `model.half()` suggestion or specify smaller output images.
### My images come out black
We might have this fixed, we are still testing.
There's a [similar issue](https://github.com/CompVis/stable-diffusion/issues/69)
on CUDA GPU's where the images come out green. Maybe it's the same issue?
Someone in that issue says to use "--precision full", but this fork
actually disables that flag. I don't know why, someone else provided
that code and I don't know what it does. Maybe the `model.half()`
suggestion above would fix this issue too. I should probably test it.
### "view size is not compatible with input tensor's size and stride"
```
File "/opt/anaconda3/envs/ldm/lib/python3.10/site-packages/torch/nn/functional.py", line 2511, in layer_norm
return torch.layer_norm(input, normalized_shape, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
```
Update to the latest version of lstein/stable-diffusion. We were
patching pytorch but we found a file in stable-diffusion that we could
change instead. This is a 32-bit vs 16-bit problem.
### The processor must support the Intel bla bla bla
What? Intel? On an Apple Silicon?
Intel MKL FATAL ERROR: This system does not meet the minimum requirements for use of the Intel(R) Math Kernel Library.
The processor must support the Intel(R) Supplemental Streaming SIMD Extensions 3 (Intel(R) SSSE3) instructions.
The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions.
The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
This is due to the Intel `mkl` package getting picked up when you try to install
something that depends on it-- Rosetta can translate some Intel instructions but
not the specialized ones here. To avoid this, make sure to use the environment
variable `CONDA_SUBDIR=osx-arm64`, which restricts the Conda environment to only
use ARM packages, and use `nomkl` as described above.
### input types 'tensor<2x1280xf32>' and 'tensor<\*xf16>' are not broadcast compatible
May appear when just starting to generate, e.g.:
```
dream> clouds
Generating: 0%| | 0/1 [00:00<?, ?it/s]/Users/[...]/dev/stable-diffusion/ldm/modules/embedding_manager.py:152: UserWarning: The operator 'aten::nonzero' is not currently supported on the MPS backend and will fall back to run on the CPU. This may have performance implications. (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1662016319283/work/aten/src/ATen/mps/MPSFallback.mm:11.)
placeholder_idx = torch.where(
loc("mps_add"("(mpsFileLoc): /AppleInternal/Library/BuildRoots/20d6c351-ee94-11ec-bcaf-7247572f23b4/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm":219:0)): error: input types 'tensor<2x1280xf32>' and 'tensor<*xf16>' are not broadcast compatible
LLVM ERROR: Failed to infer result type(s).
Abort trap: 6
/Users/[...]/opt/anaconda3/envs/ldm/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
```
Macs do not support autocast/mixed-precision. Supply `--full_precision` to use float32 everywhere.

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# **Windows Installation**
## **Notebook install (semi-automated)**
We have a [Jupyter notebook](https://github.com/lstein/stable-diffusion/blob/main/Stable-Diffusion-local-Windows.ipynb) with cell-by-cell installation steps. It will download the code in this repo as one of the steps, so instead of cloning this repo, simply download the notebook from the link above and load it up in VSCode (with the appropriate extensions installed)/Jupyter/JupyterLab and start running the cells one-by-one.
Note that you will need NVIDIA drivers, Python 3.10, and Git installed beforehand - simplified
[step-by-step instructions](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install) are available in the wiki (you'll only need steps 1, 2, & 3 ).
## **Manual Install**
### **pip**
See [Easy-peasy Windows install](https://github.com/lstein/stable-diffusion/wiki/Easy-peasy-Windows-install)
in the wiki
### **Conda**
1. Install Anaconda3 (miniconda3 version) from here: https://docs.anaconda.com/anaconda/install/windows/
2. Install Git from here: https://git-scm.com/download/win
3. Launch Anaconda from the Windows Start menu. This will bring up a command window. Type all the remaining commands in this window.
4. Run the command:
```
git clone https://github.com/lstein/stable-diffusion.git
```
This will create stable-diffusion folder where you will follow the rest of the steps.
5. Enter the newly-created stable-diffusion folder. From this step forward make sure that you are working in the stable-diffusion directory!
```
cd stable-diffusion
```
6. Run the following two commands:
```
conda env create -f environment.yaml (step 6a)
conda activate ldm (step 6b)
```
This will install all python requirements and activate the "ldm" environment which sets PATH and other environment variables properly.
7. Run the command:
```
python scripts\preload_models.py
```
This installs several machine learning models that stable diffusion requires.
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.
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.
Now run the following commands from **within the stable-diffusion 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
```
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!
```
# for the pre-release weights
python scripts\dream.py -l
# for the post-release weights
python scripts\dream.py
```
10. Subsequently, to relaunch the script, first activate the Anaconda command window (step 3),enter the stable-diffusion directory (step 5, `cd \path\to\stable-diffusion`), run `conda activate ldm` (step 6b), and then launch the dream script (step 9).
**Note:** Tildebyte has written an alternative ["Easy peasy Windows
install"](https://github.com/lstein/stable-diffusion/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!)
### 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:
```
git pull
```
This will bring your local copy into sync with the remote one.