Add Dockerfile template

Dockerfile

@@ -0,0 +1,185 @@
+#Simple NVIDIA Stability-AI Stable Diffusion Docker file
+FROM nvidia/cuda:11.6.2-devel-ubi8
+
+#Install required RPM packages
+RUN dnf install git python38 python38-devel python38-setuptools mesa-libGLU python38-pip gcc gcc-c++ -y 
+
+# Install Stability-AI's stable diffusion, and required packages
+WORKDIR /WORKDIR/
+#Try GeoDerp's Fork if you find negative prompt doesn't work: https://github.com/GeoDerp/stablediffusion.git
+RUN git clone https://github.com/Stability-AI/stablediffusion.git
+WORKDIR /WORKDIR/stablediffusion
+RUN python3 -m pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116
+RUN python3 -m pip install diffusers 
+# Bellow installs packages from requirements.txt manually (to ensure it installs). You can remove this if you have no issues
+RUN python3 -m pip install timm albumentations==0.4.3 opencv-python pudb==2019.2 imageio==2.9.0 imageio-ffmpeg==0.4.2 pytorch-lightning==1.4.2 torchmetrics==0.6 omegaconf==2.1.1 test-tube>=0.7.5 streamlit>=0.73.1 einops==0.3.0 transformers==4.19.2 webdataset==0.2.5 open-clip-torch==2.7.0 gradio==3.13.2 kornia==0.6 invisible-watermark>=0.1.5 streamlit-drawable-canvas==0.8.0 -e .
+RUN python3 -m pip install -r requirements.txt
+RUN python3 -m pip install -e .
+
+# Install xformers (this will take some time to compile)
+WORKDIR /WORKDIR/
+RUN ln -s /usr/bin/gcc-$MAX_GCC_VERSION /usr/local/cuda/bin/gcc 
+RUN ln -s /usr/bin/g++-$MAX_GCC_VERSION /usr/local/cuda/bin/g++
+ENV CUDA_HOME=/usr/local/cuda-11.6
+#Idea from https://www.reddit.com/user/AiAdventurer/ : https://www.reddit.com/r/StableDiffusion/comments/107hdon/frustration_trying_to_get_xformers_working_always/j3mcfrj/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
+ENV LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64
+ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.6/include
+ENV PATH="/usr/local/cuda-11.6/bin:$PATH"
+
+ENV TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.2;8.0;8.6"
+RUN git clone https://github.com/facebookresearch/xformers.git
+WORKDIR /WORKDIR/xformers
+RUN git submodule update --init --recursive
+# Bellow installs packages from requirements.txt manually (to ensure it installs). You can remove this if you have no issues
+RUN python3 -m pip install triton numpy
+RUN python3 -m pip install -r requirements.txt
+RUN python3 -m pip install --verbose -e .
+WORKDIR /WORKDIR/stablediffusion
+
+
+# Pre download OpenCLIP model, 
+# Idea from: https://github.com/Stability-AI/stablediffusion/issues/73#issuecomment-1343820268
+RUN cat <<'EOT' >> OpenCLIP.py
+import open_clip
+open_clip.list_pretrained()
+model, _, preprocess = open_clip.create_model_and_transforms('ViT-H-14', pretrained='laion2b_s32b_b79k')
+model.eval()
+EOT
+RUN chmod +xwr OpenCLIP.py
+RUN python3 OpenCLIP.py
+
+#Expose port for Gradio scripts *(yet to work out how to bypass this and just use cli arguments)*
+EXPOSE 7860/tcp
+EXPOSE 7860/udp
+#allow external access to web ui on gradio scripts
+RUN sed -i 's/block.launch()/block.launch(server_name="0.0.0.0")/g' ./scripts/gradio/depth2img.py
+RUN sed -i 's/block.launch()/block.launch(server_name="0.0.0.0")/g' ./scripts/gradio/inpainting.py
+RUN sed -i 's/block.launch()/block.launch(server_name="0.0.0.0")/g' ./scripts/gradio/superresolution.py
+
+
+#Create run script
+RUN cat <<'EOT' >> start.sh
+#!/bin/bash
+python3 -m xformers.info
+
+if [ -z "$CKPT" ]; then
+    echo "missing checkpoint file, ex. -e CKPT=CKPTFILENAME"
+fi 
+if [ -z "$HIGHT" ]; then
+    HIGHT=768
+fi 
+if [ -z "$WIDTH" ]; then
+    WIDTH=768
+fi 
+if [ -z "$STRENGTH" ]; then
+    STRENGTH=0.8
+fi 
+if [ -z "$STEPS" ]; then
+    STEPS=50
+fi 
+
+if [ -z "$N_ITER" ]; then
+    N_ITER=3
+fi 
+
+if [ -z "$N_SAMPLES" ]; then
+    N_SAMPLES=3
+fi 
+
+if [ -z "$SCALE" ]; then
+    SCALE=9.0
+fi 
+
+
+if [ -z "$DDIM_ETA" ]; then
+    DDIM_ETA=0.0
+fi 
+
+
+if [ -n "$SCRIPT" ]; then
+  case "$SCRIPT" in
+
+    text-to-image)
+        if [ -z "$PROMPT" ]; then
+            echo "missing prompt , ex. -e PROMPT=\"image of dog\""
+        fi
+        python3 scripts/txt2img.py --device cuda --ddim_eta $DDIM_ETA --scale $SCALE --prompt "$PROMPT" --n_iter $N_ITER --n_samples $N_SAMPLES --steps $STEPS --ckpt ./mount/$CKPT --config configs/stable-diffusion/v2-inference-v.yaml --H $HIGHT --W $WIDTH 
+    ;;
+
+    text-to-image-negative)
+        if [ -z "$PROMPT" ]; then
+            echo "missing prompt , ex. -e PROMPT=\"image of dog\""
+        fi
+        if [ -z "$N_PROMPT" ]; then
+            echo "missing negative prompt , ex. -e N_PROMPT=\"blury, low quality\""
+        fi
+        python3 scripts/txt2img.py --device cuda --ddim_eta $DDIM_ETA --scale $SCALE --prompt "$PROMPT" --n_prompt "$N_PROMPT" --n_iter $N_ITER --n_samples $N_SAMPLES  --steps $STEPS --ckpt ./mount/$CKPT --config configs/stable-diffusion/v2-inference-v.yaml --H $HIGHT --W $WIDTH 
+    ;;
+
+    depth-to-image)
+        #need pt file and model to work 
+        mkdir -p ./midas_models/ </dev/null
+        cp ./mount/*.pt ./midas_models/
+        python3 scripts/gradio/depth2img.py configs/stable-diffusion/v2-midas-inference.yaml ./mount/$CKPT
+    ;;
+
+    img-to-img)
+        if [ -z "$IMAGE" ]; then
+            echo "missing image file, ex. -e IMAGE=IMAGEFILENAME"
+        fi
+        if [ -z "$PROMPT" ]; then
+            echo "missing prompt , ex. -e PROMPT=\"image of dog\""
+        fi
+        python3 scripts/img2img.py --prompt "$PROMPT" --init-img ./mount/$IMAGE  --scale $SCALE --ddim_eta $DDIM_ETA --n_iter $N_ITER --n_samples $N_SAMPLES --ddim_steps $STEPS --strength $STRENGTH --ckpt ./mount/$CKPT
+    ;;
+
+    inpainting)
+        #https://stackoverflow.com/a/48621344
+        rm -rf ~/.nv/ </dev/null
+        python3 scripts/gradio/inpainting.py configs/stable-diffusion/v2-inpainting-inference.yaml ./mount/$CKPT
+    ;;
+
+    superresolution)
+        python3 scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml ./mount/$CKPT
+    ;;
+  esac
+
+  else
+    echo "missing script statement, ex. -e SCRIPT=text-to-image"
+    echo "options: text-to-image , depth-to-image , img-to-img , inpainting , superresolution"
+fi
+EOT
+# Give script correct permissions
+RUN chmod +xwr start.sh
+# Remove windows encoding issue
+RUN sed -i -e 's/\r$//' start.sh
+
+
+#Create folder to mount, this stores input files: ckpt/image/mask/pt files, (ex. -v <LocalFoulder>:/mount) 
+RUN mkdir mount 
+
+
+#Run start script on start
+CMD ["./start.sh"]
+
+
+
+#STEP 1, build Docker image
+#Make sure you have Docker, Nvidia and Nvidia Cuda drivers installed on host
+#copy Dockerfile to current working directory and run:
+    #docker build --tag sd-docker . 
+
+#STEP 2, run Docker Image as container
+    #docker run examples:
+        #text-to-image:             docker run --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="text-to-image"           -e PROMPT="a professional photograph of an astronaut riding a horse" -e CKPT="v2-1_768-ema-pruned.ckpt"                                             -e N_ITER=1 -e N_SAMPLES=1 -e STEPS=90 -e SCALE=14 -e HIGHT="768" -e WIDTH="768" sd-docker 
+        #text-to-image-negative:    docker run --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="text-to-image-negative"  -e PROMPT="a professional photograph of an astronaut riding a horse" -e CKPT="v2-1_768-ema-pruned.ckpt" -e N_PROMPT="blury, low quality, low res"   -e N_ITER=1 -e N_SAMPLES=1 -e STEPS=90 -e SCALE=14 -e HIGHT="768" -e WIDTH="768" sd-docker 
+        #img-to-img:                docker run --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="img-to-img"              -e PROMPT="a professional photograph of an astronaut riding a horse" -e CKPT="512-base-ema.ckpt"                                                    -e N_ITER=1 -e N_SAMPLES=1 -e STEPS=90 -e STRENGTH=0.8 -e SCALE="14" sd-docker 
+        #generates web ui: localhost:7860 
+            #depth-to-image:(need pt and ckpt)    docker run --gpus=all -p 7860:7860 -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="depth-to-image"     -e CKPT="512-depth-ema.ckpt" sd-docker 
+            #inpainting:        docker run --gpus=all -p 7860:7860 -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="inpainting"                           -e CKPT="512-inpainting-ema.ckpt" sd-docker 
+            #superresolution:   docker run --gpus=all -p 7860:7860 -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="superresolution"                      -e CKPT="x4-upscaler-ema.ckpt" sd-docker 
+
+
+#STEP 2 - alternative
+    #You may also like to run multiple/different commands in the sandboxed space. you can bypass the start script and get access to the terminal via:
+        # docker run -it -p 7860:7860 --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount --entrypoint /bin/bash sd

README.md

@@ -261,7 +261,60 @@ streamlit run scripts/streamlit/inpainting.py -- configs/stable-diffusion/v2-inp
 for a Gradio or Streamlit demo of the inpainting model. 
 This scripts adds invisible watermarking to the demo in the [RunwayML](https://github.com/runwayml/stable-diffusion/blob/main/scripts/inpaint_st.py) repository, but both should work interchangeably with the checkpoints/configs.  
 
+</br>  
 
+## Docker Install  
+*For those who struggle with installing Stable Diffusion, or like to run the sctipts containerised.*
+
+### STEP 1, build Docker image  
+- Make sure you have: `Docker`, `NVIDIA` and `NVIDIA Cuda` installed on host.
+- Copy Dockerfile to current working directory and run:
+
+```bash
+git clone https://github.com/Stability-AI/stablediffusion.git
+cd stablediffusion
+docker build --tag sd-docker . 
+```
+
+#### STEP 2, run Docker Image as container  
+*Docker run examples:*
+
+- text-to-image:  
+  ```bash
+  docker run --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="text-to-image"           -e PROMPT="a professional photograph of an astronaut riding a horse" -e CKPT="v2-1_768-ema-pruned.ckpt"                                             -e N_ITER=1 -e N_SAMPLES=1 -e STEPS=90 -e SCALE=14 -e HIGHT="768" -e WIDTH="768" sd-docker
+  ``` 
+- text-to-image-negative:  
+  ```bash
+  docker run --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="text-to-image-negative"  -e PROMPT="a professional photograph of an astronaut riding a horse" -e CKPT="v2-1_768-ema-pruned.ckpt" -e N_PROMPT="blury, low quality, low res"   -e N_ITER=1 -e N_SAMPLES=1 -e STEPS=90 -e SCALE=14 -e HIGHT="768" -e WIDTH="768" sd-docker
+  ``` 
+- img-to-img:  
+  ```bash
+  docker run --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="img-to-img"              -e PROMPT="a professional photograph of an astronaut riding a horse" -e CKPT="512-base-ema.ckpt"                                                    -e N_ITER=1 -e N_SAMPLES=1 -e STEPS=90 -e STRENGTH=0.8 -e SCALE="14" sd-docker
+  ``` 
+
+Generates Gradio Web UI: localhost:7860 
+- depth-to-image: (need pt and ckpt)  
+  ```bash
+  docker run --gpus=all -p 7860:7860 -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="depth-to-image"   -e CKPT="512-depth-ema.ckpt" sd-docker
+  ```
+- inpainting:  
+  ```bash
+  docker run --gpus=all -p 7860:7860 -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="inpainting"       -e CKPT="512-inpainting-ema.ckpt" sd-docker
+  ```
+- superresolution:  
+  ```bash
+  docker run --gpus=all -p 7860:7860 -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount -e SCRIPT="superresolution"  -e CKPT="x4-upscaler-ema.ckpt" sd-docker
+  ```
+
+#### STEP 2 - Bash alternative  
+*you may also like to run multiple/different commands in the sandboxed space. you can bypass the start script and get access to the bash terminal via:*
+```bash
+docker run -it -p 7860:7860 --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount --entrypoint /bin/bash sd
+```
+
+#### STEP 2 - Bash alternative  
+*you may also like to run multiple/different commands in the sandboxed space. you can bypass the start script and get access to the terminal via:*
+```docker run -it -p 7860:7860 --gpus=all -v <YOUR-OUTPUTS-DIRECTORY>:/WORKDIR/stablediffusion/outputs -v <YOUR-MOUNT-DIRECTORY>:/WORKDIR/stablediffusion/mount --entrypoint /bin/bash sd```
 
 ## Shout-Outs
 - Thanks to [Hugging Face](https://huggingface.co/) and in particular [Apolinário](https://github.com/apolinario)  for support with our model releases!

scripts/txt2img.py

@@ -60,6 +60,13 @@ def parse_args():
         default="a professional photograph of an astronaut riding a triceratops",
         help="the prompt to render"
     )
+    parser.add_argument(
+        "--n_prompt",
+        type=str,
+        nargs="?",
+        default="",
+        help="the negative prompt to render"
+    )
     parser.add_argument(
         "--outdir",
         type=str,
@@ -309,8 +316,11 @@ def main(opt):
         prompts = data[0]
         print("Running a forward pass to initialize optimizations")
         uc = None
-        if opt.scale != 1.0:
-            uc = model.get_learned_conditioning(batch_size * [""])
+        if opt.n_prompt == "":
+            if opt.scale != 1.0:
+                uc = model.get_learned_conditioning(batch_size * [""])
+        else:
+            uc = model.get_learned_conditioning(opt.n_prompt)
         if isinstance(prompts, tuple):
             prompts = list(prompts)