Merge branch 'main' of github.com:santisbon/stable-diffusion into santisbon-main

.github/workflows/test-dream-conda.yml

@@ -85,9 +85,9 @@ jobs:
           fi
           # Utterly hacky, but I don't know how else to do this
           if [[ ${{ github.ref }} == 'refs/heads/master' ]]; then
-            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/preflight_prompts.txt
+            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/preflight_prompts.txt --full_precision
           elif [[ ${{ github.ref }} == 'refs/heads/development' ]]; then
-            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/dev_prompts.txt
+            time ${{ steps.vars.outputs.PYTHON_BIN }} scripts/dream.py --from_file tests/dev_prompts.txt --full_precision
           fi
           mkdir -p outputs/img-samples
       - name: Archive results

.gitignore

@@ -3,6 +3,9 @@ outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
 ldm/restoration/codeformer/weights
 
+# ignore the Anaconda/Miniconda installer used while building Docker image
+anaconda.sh
+
 # ignore a directory which serves as a place for initial images
 inputs/
 

README.md

@@ -1,4 +1,4 @@
-<div align="center">
+<h1 align='center'><b>InvokeAI: A Stable Diffusion Toolkit</b></h1>
 
 # Stable Diffusion Dream Script
 
@@ -8,7 +8,7 @@
   <a href="https://discord.gg/ZmtBAhwWhy"><img src="docs/assets/join-us-on-discord-image.png"/></a>
 </p>
 
-# **Stable Diffusion Dream Script**
+# **InvokeAI - A Stable Diffusion Toolkit**
 [![discord badge]][discord link]
 
 [![latest release badge]][latest release link] [![github stars badge]][github stars link] [![github forks badge]][github forks link]

docker-build/Dockerfile

@@ -0,0 +1,57 @@
+FROM        debian 
+
+ARG         gsd
+ENV         GITHUB_STABLE_DIFFUSION $gsd
+
+ARG         rsd
+ENV         REQS $rsd
+
+ARG         cs
+ENV         CONDA_SUBDIR $cs
+
+ENV         PIP_EXISTS_ACTION="w"
+
+# TODO: Optimize image size
+
+SHELL       ["/bin/bash", "-c"]
+
+WORKDIR     /
+RUN         apt update && apt upgrade -y \
+            && apt install -y \
+            git \
+            libgl1-mesa-glx \
+            libglib2.0-0 \
+            pip \
+            python3 \
+            && git clone $GITHUB_STABLE_DIFFUSION
+
+# Install Anaconda or Miniconda
+COPY        anaconda.sh .
+RUN         bash anaconda.sh -b -u -p /anaconda && /anaconda/bin/conda init bash
+
+# SD 
+WORKDIR     /stable-diffusion 
+RUN         source ~/.bashrc \
+            && conda create -y --name ldm && conda activate ldm \
+            && conda config --env --set subdir $CONDA_SUBDIR \
+            && pip3 install -r $REQS \   
+            && pip3 install basicsr facexlib realesrgan \          
+            && mkdir models/ldm/stable-diffusion-v1 \
+            && ln -s "/data/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt 
+
+# Face restoreation
+# by default expected in a sibling directory to stable-diffusion
+WORKDIR     /       
+RUN         git clone https://github.com/TencentARC/GFPGAN.git 
+
+WORKDIR     /GFPGAN 
+RUN         pip3 install -r requirements.txt \
+            && python3 setup.py develop \
+            && ln -s "/data/GFPGANv1.3.pth" experiments/pretrained_models/GFPGANv1.3.pth
+
+WORKDIR     /stable-diffusion 
+RUN         python3 scripts/preload_models.py         
+
+WORKDIR     /
+COPY        entrypoint.sh .
+ENTRYPOINT ["/entrypoint.sh"]

docker-build/entrypoint.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cd /stable-diffusion
+
+if [ $# -eq 0 ]; then
+    python3 scripts/dream.py --full_precision -o /data
+    # bash
+else
+    python3 scripts/dream.py --full_precision -o /data "$@"
+fi

docs/features/INPAINTING.md

@@ -44,7 +44,6 @@ We are hoping to get rid of the need for this workaround in an upcoming release.
 8. In the export dialogue, Make sure the "Save colour values from
    transparent pixels" checkbox is selected.
 
----
 
 ## Recipe for Adobe Photoshop
 

docs/installation/INSTALL_DOCKER.md

@@ -0,0 +1,183 @@
+# Before you begin
+
+- For end users: Install Stable Diffusion locally using the instructions for your OS.
+- For developers: For container-related development tasks or for enabling easy deployment to other environments (on-premises or cloud), follow these instructions. For general use, install locally to leverage your machine's GPU.
+
+# Why containers?
+
+They provide a flexible, reliable way to build and deploy Stable Diffusion. You'll also use a Docker volume to store the largest model files and image outputs as a first step in decoupling storage and compute. Future enhancements can do this for other assets. See [Processes](https://12factor.net/processes) under the Twelve-Factor App methodology for details on why running applications in such a stateless fashion is important.
+
+You can specify the target platform when building the image and running the container. You'll also need to specify the Stable Diffusion requirements file that matches the container's OS and the architecture it will run on.  
+
+Developers on Apple silicon (M1/M2): You [can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224) and performance is reduced compared with running it directly on macOS but for development purposes it's fine. Once you're done with development tasks on your laptop you can build for the target platform and architecture and deploy to another environment with NVIDIA GPUs on-premises or in the cloud.  
+
+# Installation on a Linux container 
+
+## Prerequisites
+
+### Get the data files  
+
+Go to [Hugging Face](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original), and click "Access repository" to Download the model file ```sd-v1-4.ckpt``` (~4 GB) to ```~/Downloads```. You'll need to create an account but it's quick and free.  
+
+Also download the face restoration model.
+```Shell
+cd ~/Downloads
+wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth
+```
+
+### Install [Docker](https://github.com/santisbon/guides#docker)  
+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.  
+
+## Setup
+
+Set the fork you want to use and other variables.  
+```Shell
+TAG_STABLE_DIFFUSION="santisbon/stable-diffusion"
+PLATFORM="linux/arm64"
+GITHUB_STABLE_DIFFUSION="-b orig-gfpgan https://github.com/santisbon/stable-diffusion.git"
+REQS_STABLE_DIFFUSION="requirements-linux-arm64.txt"
+CONDA_SUBDIR="osx-arm64"
+
+echo $TAG_STABLE_DIFFUSION
+echo $PLATFORM
+echo $GITHUB_STABLE_DIFFUSION
+echo $REQS_STABLE_DIFFUSION
+echo $CONDA_SUBDIR
+```
+
+Create a Docker volume for the downloaded model files.
+```Shell
+docker volume create my-vol
+```
+
+Copy the data files to the Docker volume using a lightweight Linux container. We'll need the models at run time. You just need to create the container with the mountpoint; no need to run this dummy container.
+```Shell
+cd ~/Downloads # or wherever you saved the files
+
+docker create --platform $PLATFORM --name dummy --mount source=my-vol,target=/data alpine 
+
+docker cp sd-v1-4.ckpt dummy:/data
+docker cp GFPGANv1.3.pth dummy:/data
+```
+
+Get the repo and download the Miniconda installer (we'll need it at build time). Replace the URL with the version matching your container OS and the architecture it will run on.
+```Shell
+cd ~
+git clone $GITHUB_STABLE_DIFFUSION
+
+cd stable-diffusion/docker-build
+chmod +x entrypoint.sh
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O anaconda.sh && chmod +x anaconda.sh
+```
+
+Build the Docker image. Give it any tag ```-t``` that you want.  
+Choose the Linux container's host platform: x86-64/Intel is ```amd64```. Apple silicon is ```arm64```. If deploying the container to the cloud to leverage powerful GPU instances you'll be on amd64 hardware but if you're just trying this out locally on Apple silicon choose arm64.  
+The application uses libraries that need to match the host environment so use the appropriate requirements file.  
+Tip: Check that your shell session has the env variables set above.  
+```Shell
+docker build -t $TAG_STABLE_DIFFUSION \
+--platform $PLATFORM \
+--build-arg gsd=$GITHUB_STABLE_DIFFUSION \
+--build-arg rsd=$REQS_STABLE_DIFFUSION \
+--build-arg cs=$CONDA_SUBDIR \
+.
+```
+
+Run a container using your built image.  
+Tip: Make sure you've created and populated the Docker volume (above).
+```Shell
+docker run -it \
+--rm \
+--platform $PLATFORM \
+--name stable-diffusion \
+--hostname stable-diffusion \
+--mount source=my-vol,target=/data \
+$TAG_STABLE_DIFFUSION
+```
+
+# Usage (time to have fun)
+
+## 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```:  
+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/```.
+```Shell
+python3 scripts/dream.py --full_precision  
+```
+
+You'll get the script's prompt. You can see available options or quit.
+```Shell
+dream> -h
+dream> q
+```
+
+## Text to Image
+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.  
+The prompt can be in quotes or not.
+```Shell
+dream> The hulk fighting with sheldon cooper -s5 -n1 
+dream> "woman closeup highly detailed"  -s 150
+# Reuse previous seed and apply face restoration
+dream> "woman closeup highly detailed"  --steps 150 --seed -1 -G 0.75 
+```
+
+You'll need to experiment to see if face restoration is making it better or worse for your specific prompt.
+
+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.  
+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.  
+
+On your host Mac (you can use the name of any container that mounted the volume):
+```Shell
+docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures 
+```
+
+## Image to Image
+You can also do text-guided image-to-image translation. For example, turning a sketch into a detailed drawing.  
+
+```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.  
+
+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.  
+
+If you're on a Docker container, copy your input image into the Docker volume
+```Shell
+docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
+```
+
+Try it out generating an image (or more). The ```dream``` script needs absolute paths to find the image so don't use ```~```.  
+
+If you're on your Mac
+```Shell 
+dream> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75  --steps 100 -n4
+```
+If you're on a Linux container on your Mac
+```Shell
+dream> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75  --steps 50 -n1
+```
+
+## Web Interface
+You can use the ```dream``` script with a graphical web interface. Start the web server with:
+```Shell
+python3 scripts/dream.py --full_precision --web
+```
+If it's running on your Mac point your Mac web browser to http://127.0.0.1:9090  
+
+Press Control-C at the command line to stop the web server.
+
+## Notes
+
+Some text you can add at the end of the prompt to make it very pretty:
+```Shell
+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, 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
+```
+
+The original scripts should work as well.
+```Shell
+python3 scripts/orig_scripts/txt2img.py --help
+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"
+python3 scripts/orig_scripts/txt2img.py --ddim_steps 5   --n_iter 1 --n_samples 1  --plms --prompt "ocean" # or --klms
+```

requirements-linux-arm64.txt

@@ -0,0 +1,25 @@
+albumentations==0.4.3
+einops==0.3.0
+huggingface-hub==0.8.1
+imageio==2.9.0
+imageio-ffmpeg==0.4.2
+kornia==0.6.0
+numpy==1.23.1
+--pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+omegaconf==2.1.1
+opencv-python==4.6.0.66
+pillow==9.2.0
+pudb==2019.2
+torch==1.12.1
+torchvision==0.13.0
+pytorch-lightning==1.4.2
+streamlit==1.12.0
+test-tube>=0.7.5
+torch-fidelity==0.3.0
+torchmetrics==0.6.0
+transformers==4.19.2
+-e git+https://github.com/openai/CLIP.git@main#egg=clip
+-e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
+git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
+-e git+https://github.com/lstein/GFPGAN@fix-dark-cast-images#egg=gfpgan
+-e .