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Add entrypoint and update documentation
This commit is contained in:
Armando C. Santisbon
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7136603604
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b656d333de
@ -2,29 +2,84 @@
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Table of Contents
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=================
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* [Installation](#installation)
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* [Option 1 - Directly on Apple silicon](#option-1---directly-on-apple-silicon)
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* [Step 1 - Get the Model](#step-1---get-the-model)
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* [Step 2 - Installation](#step-2---installation)
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* [Option A - On a Linux container with Docker for Apple silicon](#option-a---on-a-linux-container-with-docker-for-apple-silicon)
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* [Prerequisites](#prerequisites)
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* [Setup](#setup)
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* [Option 2 - On a Linux container with Docker for Apple silicon](#option-2---on-a-linux-container-with-docker-for-apple-silicon)
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* [Option B - Directly on Apple silicon](#option-b---directly-on-apple-silicon)
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* [Prerequisites](#prerequisites-1)
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* [Setup](#setup-1)
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* [[Optional] Face Restoration and Upscaling](#optional-face-restoration-and-upscaling)
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* [Setup](#setup-2)
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* [Usage](#usage)
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* [Step 3 - Usage (time to have fun)](#step-3---usage-time-to-have-fun)
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* [Startup](#startup)
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* [Text to Image](#text-to-image)
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* [Image to Image](#image-to-image)
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* [Web Interface](#web-interface)
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* [Notes](#notes)
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# Step 1 - Get the Model
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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```.
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You'll need to create an account but it's quick and free.
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# Installation
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# Step 2 - Installation
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## Option 1 - Directly on Apple silicon
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## Option A - On a Linux container with Docker for Apple silicon
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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.
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### Prerequisites
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[Install Docker](https://gist.github.com/santisbon/2165fd1c9aaa1f7974f424535d3756f7#install-2)
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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.
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Create a Docker volume for the downloaded model file
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```
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docker volume create my-vol
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```
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Populate the volume using a lightweight Linux container. You just need to create the container with the mountpoint; no need to run it.
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```Shell
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docker create --platform linux/arm64 --name dummy --mount source=my-vol,target=/data alpine # or arm64v8/alpine
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# Copy the model file to the Docker volume. We'll need it at run time.
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cd ~/Downloads # or wherever you saved sd-v1-4.ckpt
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docker cp sd-v1-4.ckpt dummy:/data
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```
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### Setup
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```Shell
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# Set the fork you want to use.
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GITHUB_STABLE_DIFFUSION="https://github.com/santisbon/stable-diffusion.git"
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git clone $GITHUB_STABLE_DIFFUSION
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cd stable-diffusion
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chmod +x entrypoint.sh
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# download the Miniconda installer. We'll need it at build time.
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh \
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```
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Build the Docker image. Give it any tag ```-t``` that you want.
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Tip: Make sure your shell session has the env variable set (above) with ```echo $GITHUB_STABLE_DIFFUSION```.
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```Shell
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docker build -t santisbon/stable-diffusion \
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--build-arg gsd=$GITHUB_STABLE_DIFFUSION \
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--build-arg sdreq="requirements-linux-arm64.txt" \
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.
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```
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Run a container using your built image e.g.
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```Shell
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docker run -it \
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--rm \
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--platform linux/arm64 \
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--name stable-diffusion \
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--hostname stable-diffusion \
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--mount source=my-vol,target=/data \
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--expose 9090 \
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--publish 9090:9090 \
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santisbon/stable-diffusion
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```
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Tip: Make sure you've created the Docker volume (above)
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## Option B - Directly on Apple silicon
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For Mac M1/M2. Read more about [Metal Performance Shaders (MPS) framework](https://developer.apple.com/documentation/metalperformanceshaders).
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### Prerequisites
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@ -40,7 +95,7 @@ conda init zsh && source ~/.zshrc # or bash and .bashrc
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```Shell
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# Set the fork you want to use.
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GITHUB_STABLE_DIFFUSION=https://github.com/santisbon/stable-diffusion.git
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GITHUB_STABLE_DIFFUSION="https://github.com/santisbon/stable-diffusion.git"
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git clone $GITHUB_STABLE_DIFFUSION
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cd stable-diffusion
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@ -53,8 +108,8 @@ ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
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# When path exists, pip3 will (w)ipe.
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# restrict the Conda environment to only use ARM packages. M1/M2 is ARM-based. You could also conda install nomkl.
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PIP_EXISTS_ACTION=w
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CONDA_SUBDIR=osx-arm64
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PIP_EXISTS_ACTION="w"
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CONDA_SUBDIR="osx-arm64"
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conda env create -f environment-mac.yaml && conda activate ldm
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```
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@ -63,92 +118,15 @@ You can verify you're in the virtual environment by looking at which executable
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type python3
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```
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Only need to do this once:
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Face Restoration and Upscaling
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```Shell
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python3 scripts/preload_models.py
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```
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## Option 2 - On a Linux container with Docker for Apple silicon
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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.
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### Prerequisites
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[Install Docker](https://gist.github.com/santisbon/2165fd1c9aaa1f7974f424535d3756f7#install-2)
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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.
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Create a Docker volume for the downloaded model file
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```
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docker volume create my-vol
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```
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Populate the volume using a lightweight Linux container. You just need to create the container with the mountpoint; no need to run it.
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```Shell
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docker create --platform linux/arm64 --name dummy --mount source=my-vol,target=/data alpine # or arm64v8/alpine
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cd ~/Downloads # or wherever you saved sd-v1-4.ckpt
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docker cp sd-v1-4.ckpt dummy:/data
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```
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### Setup
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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.
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```Shell
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docker run -it \
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--platform linux/arm64 \
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--name stable-diffusion \
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--hostname stable-diffusion \
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--mount source=my-vol,target=/data \
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--expose 9090 \
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--publish 80:9090 \
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debian
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# or arm64v8/debian
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```
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You're now on the container.
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```Shell
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# Set the fork you want to use
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GITHUB_STABLE_DIFFUSION="-b docker-apple-silicon https://github.com/santisbon/stable-diffusion.git" \
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&& apt update && apt upgrade -y \
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&& apt install -y \
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git \
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pip \
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python3 \
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wget
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# you won't need to close and reopen your terminal after this because we'll source our .<shell>rc file
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cd /data && wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh \
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&& chmod +x anaconda.sh && bash anaconda.sh -b -u -p /anaconda && /anaconda/bin/conda init bash && source ~/.bashrc
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cd / && git clone $GITHUB_STABLE_DIFFUSION && cd stable-diffusion
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# When path exists, pip3 will (w)ipe.
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# restrict the Conda environment to only use ARM packages. M1/M2 is ARM-based. You could also conda install nomkl.
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PIP_EXISTS_ACTION=w
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CONDA_SUBDIR=osx-arm64
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# Create the environment, activate it, install requirements.
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conda create -y --name ldm && conda activate ldm \
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&& pip3 install -r requirements-linux-arm64.txt
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# Only need to do this once (ok twice if you decide to add face restoration and upscaling):
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python3 scripts/preload_models.py
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mkdir -p models/ldm/stable-diffusion-v1 \
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&& chown root:root /data/sd-v1-4.ckpt \
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&& ln -sf /data/sd-v1-4.ckpt models/ldm/stable-diffusion-v1/model.ckpt
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```
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## [Optional] Face Restoration and Upscaling
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Whether you're directly on macOS or a Linux container.
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### Setup
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```Shell
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# If you're on a Linux container
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apt install -y libgl1-mesa-glx libglib2.0-0
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# by default expected in a sibling directory to stable-diffusion
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cd .. && git clone https://github.com/TencentARC/GFPGAN.git && cd GFPGAN
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# basicsr: used for training and inference. facexlib: face detection / face restoration helper.
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pip3 install basicsr facexlib
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pip3 install -r requirements.txt
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pip3 install basicsr facexlib \
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&& pip3 install -r requirements.txt
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python3 setup.py develop
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pip3 install realesrgan # to enhance the background (non-face) regions and do upscaling
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@ -156,24 +134,26 @@ pip3 install realesrgan # to enhance the background (non-face) regions and do up
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wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models
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cd ../stable-diffusion
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# 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.
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```
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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```.
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```Shell
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python3 scripts/preload_models.py
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```
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# Usage
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# Step 3 - Usage (time to have fun)
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## Startup
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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.
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If you're directly on macOS follow these startup instructions.
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With the Conda environment activated (```conda activate ldm```), run the interactive interface that combines the functionality of the original scripts txt2img and img2img:
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Use the more accurate but VRAM-intensive full precision math because half-precision requires autocast and won't work.
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By default the images are saved in ```outputs/img-samples/```.
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If you're on a docker container set the output dir to the Docker volume you created.
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```Shell
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# If on Macbook
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python3 scripts/dream.py --full_precision
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# If on Linux container
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python3 scripts/dream.py --full_precision -o /data
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# By default the images are saved in outputs/img-samples/.
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```
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You'll get the script's prompt. You can see available options or quit.
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@ -183,7 +163,7 @@ dream> q
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```
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## Text to Image
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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.
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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.
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Then increase steps to 100 or more for good (but slower) results.
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The prompt can be in quotes or not.
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```Shell
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@ -194,11 +174,11 @@ dream> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75
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# TODO: example for upscaling.
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```
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You'll need to experiment to see if face restoration is making it better or worse for your specific prompt.
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The -U option for upscaling has an [Issue](https://github.com/lstein/stable-diffusion/issues/297) on Mac.
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The -U option for upscaling has an [Issue](https://github.com/lstein/stable-diffusion/issues/297).
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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.
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If you're on a container the output is set to the Docker volume. You can copy it wherever you want.
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You can download it from the Docker Desktop app, Volumes, my-vol, data.
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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:
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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:
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```Shell
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# On your host Macbook (you can use the name of any container that mounted the volume)
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docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
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@ -1,8 +1,10 @@
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#!/bin/bash
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ln -sf /data/sd-v1-4.ckpt /stable-diffusion/models/ldm/stable-diffusion-v1/model.ckpt
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cd /stable-diffusion
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conda activate ldm
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if [ $# -eq 0 ]; then
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python3 /stable-diffusion/scripts/dream.py --full_precision -o /data
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python3 scripts/dream.py --full_precision -o /data
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else
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python3 /stable-diffusion/scripts/dream.py --full_precision -o /data "$@"
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python3 scripts/dream.py --full_precision -o /data "$@"
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fi
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