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Add README for Mac-Docker
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Armando C. Santisbon
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README-Mac-Docker.md
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README-Mac-Docker.md
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Table of Contents
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=================
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Tested on **MacBook Air M2** with **Docker Desktop for Mac with Apple Chip**.
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* [Setup](#setup)
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* [Directly on Apple silicon](#directly-on-apple-silicon)
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* [Prerequisites](#prerequisites)
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* [Set up](#set-up)
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* [On a Linux container with Docker for Apple silicon](#on-a-linux-container-with-docker-for-apple-silicon)
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* [Prerequisites](#prerequisites-1)
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* [Launch and set up a container](#launch-and-set-up-a-container)
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* [[Optional] Face Restoration and Upscaling](#optional-face-restoration-and-upscaling)
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* [Usage](#usage)
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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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# Setup
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## 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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Install the latest versions of macOS, [Homebrew](https://brew.sh/), [Python](https://gist.github.com/santisbon/2165fd1c9aaa1f7974f424535d3756f7#python), and [Git](https://gist.github.com/santisbon/2165fd1c9aaa1f7974f424535d3756f7#git).
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```Shell
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brew install cmake protobuf rust
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brew install --cask miniconda
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conda init zsh && source ~/.zshrc # or bash and .bashrc
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```
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### Set up
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```Shell
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GITHUB_STABLE_DIFFUSION=https://github.com/santisbon/stable-diffusion.git
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git clone $GITHUB_STABLE_DIFFUSION
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cd stable-diffusion
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mkdir -p models/ldm/stable-diffusion-v1/
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```
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Go to [Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), and click "Access repository" to Download ```sd-v1-4.ckpt``` (~4 GB). You'll need to create an account but it's quick and free. Then set up the environment:
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```Shell
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PATH_TO_CKPT="$HOME/Downloads" # or wherever you saved sd-v1-4.ckpt
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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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conda env create -f environment-mac.yaml && conda activate ldm
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```
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You can verify you're in the virtual environment by looking at which executable you're getting:
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```Shell
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type python3
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```
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Only need to do this once:
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```Shell
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python3 scripts/preload_models.py
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```
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## 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. Adjust the CPUs and Memory to the largest amount available 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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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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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 --name dummy --mount source=my-vol,target=/data 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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### Launch and set up a container
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Start a container for Stable Diffusion
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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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debian
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# or arm64v8/debian
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```
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You're now on the container. Set it up:
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```Shell
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apt update && apt upgrade -y && apt install -y \
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git \
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pip3 \
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python3 \
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wget
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GITHUB_STABLE_DIFFUSION="-b docker-apple-silicon https://github.com/santisbon/stable-diffusion.git"
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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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# update conda
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conda update -y -n base -c defaults conda
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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
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# conda env create -f environment.yaml && conda activate ldm
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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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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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```Shell
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cd .. # by default expected in a sibling directory
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git clone https://github.com/TencentARC/GFPGAN.git
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cd GFPGAN
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pip3 install basicsr # used for training and inference
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pip3 install facexlib # face detection and face restoration helper
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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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# pre-trained model needed for face restoration
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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 ..
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cd stable-diffusion
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python3 scripts/preload_models.py # if not, 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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# Usage
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## Startup
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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.
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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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```
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You'll get the script's prompt. You can see available options or quit.
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```Shell
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dream> -h
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dream> q
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```
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## Text to Image
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For quick (and rough) results test with 5 steps (default 50), 1 sample image.
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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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```
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dream> The hulk fighting with sheldon cooper -s5 -n1
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dream> "woman closeup highly detailed" -s 150
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# Reuse previous seed and apply face restoration (if you installed GFPGAN)
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dream> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.8
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```
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TODO: example for upscaling. The -U option currently [doesn't work](https://github.com/lstein/stable-diffusion/issues/297) on Mac.
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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 easily wherever you want.
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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/ ~/Pictures
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```
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## Image to Image
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You can also do text-guided image-to-image translation. For example, turning a sketch into a detailed drawing.
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Strength is a value between 0.0 and 1.0, that controls the amount of noise that is added to the input image. Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
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Make sure your input image size dimensions are multiples of 64 e.g. 512x512. Otherwise you'll get ```Error: product of dimension sizes > 2**31'```. If you still get the error [try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.) like 512x256.
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If you're on a docker container, copy your input image into the Docker volume
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```Shell
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docker cp ~/Pictures/sketch-mountains-input.jpg dummy:/data/
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```
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Try it out generating an image (or 4).
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```Shell
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# If you're on your Macbook
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dream> "A fantasy landscape, trending on artstation" -I ~/Pictures/sketch-mountains-input.jpg --strength 0.8 --steps 100 -n4
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# If you're on a Linux container on your Macbook
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dream> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.8 --steps 100 -n1
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```
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## Web Interface
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You can use the script with a graphical web interface
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```Shell
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python3 scripts/dream.py --full_precision --web
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```
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and point your browser to http://127.0.0.1:9090
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## Notes
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Some text you can add at the end of the prompt to make it very pretty:
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```Shell
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cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed concept art painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida
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```
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The original scripts should work as well.
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```Shell
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python3 scripts/orig_scripts/txt2img.py --help
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python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1 --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray"
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python3 scripts/orig_scripts/txt2img.py --ddim_steps 5 --n_iter 1 --n_samples 1 --plms --prompt "ocean" # or --klms
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```
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