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Add entrypoint and update documentation

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Armando C. Santisbon 2022-09-10 13:56:18 -05:00
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README-Mac-Docker.md
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@ -2,29 +2,84 @@
Table of Contents Table of Contents
================= =================
* [Installation](#installation) * [Step 1 - Get the Model](#step-1---get-the-model)
* [Option 1 - Directly on Apple silicon](#option-1---directly-on-apple-silicon) * [Step 2 - Installation](#step-2---installation)
* [Option A - On a Linux container with Docker for Apple silicon](#option-a---on-a-linux-container-with-docker-for-apple-silicon)
* [Prerequisites](#prerequisites) * [Prerequisites](#prerequisites)
* [Setup](#setup) * [Setup](#setup)
* [Option 2 - On a Linux container with Docker for Apple silicon](#option-2---on-a-linux-container-with-docker-for-apple-silicon) * [Option B - Directly on Apple silicon](#option-b---directly-on-apple-silicon)
* [Prerequisites](#prerequisites-1) * [Prerequisites](#prerequisites-1)
* [Setup](#setup-1) * [Setup](#setup-1)
* [[Optional] Face Restoration and Upscaling](#optional-face-restoration-and-upscaling) * [Step 3 - Usage (time to have fun)](#step-3---usage-time-to-have-fun)
* [Setup](#setup-2)
* [Usage](#usage)
* [Startup](#startup) * [Startup](#startup)
* [Text to Image](#text-to-image) * [Text to Image](#text-to-image)
* [Image to Image](#image-to-image) * [Image to Image](#image-to-image)
* [Web Interface](#web-interface) * [Web Interface](#web-interface)
* [Notes](#notes) * [Notes](#notes)
# Step 1 - Get the Model
Go to [Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), and click "Access repository" to Download ```sd-v1-4.ckpt``` (~4 GB) to ```~/Downloads```. Go to [Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), and click "Access repository" to Download ```sd-v1-4.ckpt``` (~4 GB) to ```~/Downloads```.
You'll need to create an account but it's quick and free. You'll need to create an account but it's quick and free.
# Installation # Step 2 - Installation
## Option 1 - Directly on Apple silicon ## Option A - On a Linux container with Docker for Apple silicon
You [can't access the Macbook M1/M2 GPU cores from the Docker containers](https://github.com/pytorch/pytorch/issues/81224) so performance is reduced but for development purposes it's fine.
### Prerequisites
[Install Docker](https://gist.github.com/santisbon/2165fd1c9aaa1f7974f424535d3756f7#install-2)
On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the CPUs and Memory to avoid this [Issue](https://github.com/lstein/stable-diffusion/issues/342). You may need to increase Swap and Disk image size too.
Create a Docker volume for the downloaded model file
```
docker volume create my-vol
```
Populate the volume using a lightweight Linux container. You just need to create the container with the mountpoint; no need to run it.
```Shell
docker create --platform linux/arm64 --name dummy --mount source=my-vol,target=/data alpine # or arm64v8/alpine
# Copy the model file to the Docker volume. We'll need it at run time.
cd ~/Downloads # or wherever you saved sd-v1-4.ckpt
docker cp sd-v1-4.ckpt dummy:/data
```
### Setup
```Shell
# Set the fork you want to use.
GITHUB_STABLE_DIFFUSION="https://github.com/santisbon/stable-diffusion.git"
git clone $GITHUB_STABLE_DIFFUSION
cd stable-diffusion
chmod +x entrypoint.sh
# download the Miniconda installer. We'll need it at build time.
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh \
```
Build the Docker image. Give it any tag ```-t``` that you want.
Tip: Make sure your shell session has the env variable set (above) with ```echo $GITHUB_STABLE_DIFFUSION```.
```Shell
docker build -t santisbon/stable-diffusion \
--build-arg gsd=$GITHUB_STABLE_DIFFUSION \
--build-arg sdreq="requirements-linux-arm64.txt" \
.
```
Run a container using your built image e.g.
```Shell
docker run -it \
--rm \
--platform linux/arm64 \
--name stable-diffusion \
--hostname stable-diffusion \
--mount source=my-vol,target=/data \
--expose 9090 \
--publish 9090:9090 \
santisbon/stable-diffusion
```
Tip: Make sure you've created the Docker volume (above)
## Option B - Directly on Apple silicon
For Mac M1/M2. Read more about [Metal Performance Shaders (MPS) framework](https://developer.apple.com/documentation/metalperformanceshaders). For Mac M1/M2. Read more about [Metal Performance Shaders (MPS) framework](https://developer.apple.com/documentation/metalperformanceshaders).
### Prerequisites ### Prerequisites
@ -40,7 +95,7 @@ conda init zsh && source ~/.zshrc # or bash and .bashrc
```Shell ```Shell
# Set the fork you want to use. # Set the fork you want to use.
GITHUB_STABLE_DIFFUSION=https://github.com/santisbon/stable-diffusion.git GITHUB_STABLE_DIFFUSION="https://github.com/santisbon/stable-diffusion.git"
git clone $GITHUB_STABLE_DIFFUSION git clone $GITHUB_STABLE_DIFFUSION
cd stable-diffusion cd stable-diffusion
@ -53,8 +108,8 @@ ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
# When path exists, pip3 will (w)ipe. # When path exists, pip3 will (w)ipe.
# restrict the Conda environment to only use ARM packages. M1/M2 is ARM-based. You could also conda install nomkl. # restrict the Conda environment to only use ARM packages. M1/M2 is ARM-based. You could also conda install nomkl.
PIP_EXISTS_ACTION=w PIP_EXISTS_ACTION="w"
CONDA_SUBDIR=osx-arm64 CONDA_SUBDIR="osx-arm64"
conda env create -f environment-mac.yaml && conda activate ldm conda env create -f environment-mac.yaml && conda activate ldm
``` ```
@ -63,92 +118,15 @@ You can verify you're in the virtual environment by looking at which executable
type python3 type python3
``` ```
Only need to do this once: Face Restoration and Upscaling
```Shell ```Shell
python3 scripts/preload_models.py
```
## Option 2 - On a Linux container with Docker for Apple silicon
You [can't access the Macbook M1/M2 GPU cores from the Docker containers](https://github.com/pytorch/pytorch/issues/81224) so performance is reduced but for development purposes it's fine.
### Prerequisites
[Install Docker](https://gist.github.com/santisbon/2165fd1c9aaa1f7974f424535d3756f7#install-2)
On the Docker Desktop app, go to Preferences, Resources, Advanced. Increase the CPUs and Memory to avoid this [Issue](https://github.com/lstein/stable-diffusion/issues/342). You may need to increase Swap and Disk image size too.
Create a Docker volume for the downloaded model file
```
docker volume create my-vol
```
Populate the volume using a lightweight Linux container. You just need to create the container with the mountpoint; no need to run it.
```Shell
docker create --platform linux/arm64 --name dummy --mount source=my-vol,target=/data alpine # or arm64v8/alpine
cd ~/Downloads # or wherever you saved sd-v1-4.ckpt
docker cp sd-v1-4.ckpt dummy:/data
```
### Setup
Start a container for Stable Diffusion. The container's 9090 port is mapped to the host's 80. That way you'll be able to use the Web interface from your Mac.
```Shell
docker run -it \
--platform linux/arm64 \
--name stable-diffusion \
--hostname stable-diffusion \
--mount source=my-vol,target=/data \
--expose 9090 \
--publish 80:9090 \
debian
# or arm64v8/debian
```
You're now on the container.
```Shell
# Set the fork you want to use
GITHUB_STABLE_DIFFUSION="-b docker-apple-silicon https://github.com/santisbon/stable-diffusion.git" \
&& apt update && apt upgrade -y \
&& apt install -y \
git \
pip \
python3 \
wget
# you won't need to close and reopen your terminal after this because we'll source our .<shell>rc file
cd /data && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh \
&& chmod +x anaconda.sh && bash anaconda.sh -b -u -p /anaconda && /anaconda/bin/conda init bash && source ~/.bashrc
cd / && git clone $GITHUB_STABLE_DIFFUSION && cd stable-diffusion
# When path exists, pip3 will (w)ipe.
# restrict the Conda environment to only use ARM packages. M1/M2 is ARM-based. You could also conda install nomkl.
PIP_EXISTS_ACTION=w
CONDA_SUBDIR=osx-arm64
# Create the environment, activate it, install requirements.
conda create -y --name ldm && conda activate ldm \
&& pip3 install -r requirements-linux-arm64.txt
# Only need to do this once (ok twice if you decide to add face restoration and upscaling):
python3 scripts/preload_models.py
mkdir -p models/ldm/stable-diffusion-v1 \
&& chown root:root /data/sd-v1-4.ckpt \
&& ln -sf /data/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
```
## [Optional] Face Restoration and Upscaling
Whether you're directly on macOS or a Linux container.
### Setup
```Shell
# If you're on a Linux container
apt install -y libgl1-mesa-glx libglib2.0-0
# by default expected in a sibling directory to stable-diffusion # by default expected in a sibling directory to stable-diffusion
cd .. && git clone https://github.com/TencentARC/GFPGAN.git && cd GFPGAN cd .. && git clone https://github.com/TencentARC/GFPGAN.git && cd GFPGAN
# basicsr: used for training and inference. facexlib: face detection / face restoration helper. # basicsr: used for training and inference. facexlib: face detection / face restoration helper.
pip3 install basicsr facexlib pip3 install basicsr facexlib \
pip3 install -r requirements.txt && pip3 install -r requirements.txt
python3 setup.py develop python3 setup.py develop
pip3 install realesrgan # to enhance the background (non-face) regions and do upscaling pip3 install realesrgan # to enhance the background (non-face) regions and do upscaling
@ -156,24 +134,26 @@ pip3 install realesrgan # to enhance the background (non-face) regions and do up
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models
cd ../stable-diffusion cd ../stable-diffusion
# if we don't preload models it will download model files from the Internet the first time you run dream.py with GFPGAN and Real-ESRGAN turned on.
python3 scripts/preload_models.py
``` ```
Only need to do this once. If we don't preload models it will download model files from the Internet when you run ```dream.py```.
```Shell
python3 scripts/preload_models.py
```
# Usage # Step 3 - Usage (time to have fun)
## Startup ## Startup
If you're on a Linux container the ```dream``` script is automatically started and the output dir set to the Docker volume you created earlier.
If you're directly on macOS follow these startup instructions.
With the Conda environment activated (```conda activate ldm```), run the interactive interface that combines the functionality of the original scripts txt2img and img2img: With the Conda environment activated (```conda activate ldm```), run the interactive interface that combines the functionality of the original scripts txt2img and img2img:
Use the more accurate but VRAM-intensive full precision math because half-precision requires autocast and won't work. Use the more accurate but VRAM-intensive full precision math because half-precision requires autocast and won't work.
By default the images are saved in ```outputs/img-samples/```.
If you're on a docker container set the output dir to the Docker volume you created.
```Shell ```Shell
# If on Macbook # If on Macbook
python3 scripts/dream.py --full_precision python3 scripts/dream.py --full_precision
# If on Linux container # By default the images are saved in outputs/img-samples/.
python3 scripts/dream.py --full_precision -o /data
``` ```
You'll get the script's prompt. You can see available options or quit. You'll get the script's prompt. You can see available options or quit.
@ -183,7 +163,7 @@ dream> q
``` ```
## Text to Image ## Text to Image
For quick (but very rough) results test with 5 steps (default 50) and 1 sample image. This will let you know that everything is set up correctly. 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.
Then increase steps to 100 or more for good (but slower) results. Then increase steps to 100 or more for good (but slower) results.
The prompt can be in quotes or not. The prompt can be in quotes or not.
```Shell ```Shell
@ -194,11 +174,11 @@ dream> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75
# TODO: example for upscaling. # TODO: example for upscaling.
``` ```
You'll need to experiment to see if face restoration is making it better or worse for your specific prompt. You'll need to experiment to see if face restoration is making it better or worse for your specific prompt.
The -U option for upscaling has an [Issue](https://github.com/lstein/stable-diffusion/issues/297) on Mac. The -U option for upscaling has an [Issue](https://github.com/lstein/stable-diffusion/issues/297).
If you're on a container and set the output to the Docker volume (or moved it there with ```mv outputs/img-samples/ /data/```) you can copy it wherever you want. If you're on a container the output is set to the Docker volume. You can copy it wherever you want.
You can download it from the Docker Desktop app, Volumes, my-vol, data. You can download it from the Docker Desktop app, Volumes, my-vol, data.
Or you can copy it from your terminal. Keep in mind ```docker cp``` can't expand ```*.png``` so you'll need to specify the image file name: Or you can copy it from your Mac terminal. Keep in mind ```docker cp``` can't expand ```*.png``` so you'll need to specify the image file name:
```Shell ```Shell
# On your host Macbook (you can use the name of any container that mounted the volume) # On your host Macbook (you can use the name of any container that mounted the volume)
docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures

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entrypoint.sh
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#!/bin/bash #!/bin/bash
ln -sf /data/sd-v1-4.ckpt /stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt ln -sf /data/sd-v1-4.ckpt /stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt
cd /stable-diffusion
conda activate ldm
if [ $# -eq 0 ]; then if [ $# -eq 0 ]; then
python3 /stable-diffusion/scripts/dream.py --full_precision -o /data python3 scripts/dream.py --full_precision -o /data
else else
python3 /stable-diffusion/scripts/dream.py --full_precision -o /data "$@" python3 scripts/dream.py --full_precision -o /data "$@"
fi fi