v21.5.7

.dockerignore

@@ -0,0 +1,6 @@
+.cache/
+cudnn_windows/
+bitsandbytes_windows/
+dataset/
+__pycache__/
+venv/

Dockerfile

@@ -0,0 +1,40 @@
+FROM nvcr.io/nvidia/pytorch:23.04-py3 as base
+ENV DEBIAN_FRONTEND=noninteractive
+ENV TZ=Europe/London
+
+RUN apt-get update && \
+    apt-get install -y git curl python3-venv python3-tk libgl1 libglib2.0-0 libgoogle-perftools-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN useradd -m -s /bin/bash appuser
+USER appuser
+
+WORKDIR /app
+
+RUN python3 -m venv ./venv && . ./venv/bin/activate && \
+    python3 -m pip install wheel
+
+# Install requirements
+COPY requirements.txt setup.py .
+RUN . ./venv/bin/activate && \
+    python3 -m pip install --use-pep517 -U -r requirements.txt
+
+# Upgrade to Torch 2.0
+RUN . ./venv/bin/activate && \
+    python3 -m pip install --use-pep517 --no-deps -U triton==2.0.0 torch>=2.0.0+cu121 xformers==0.0.17 \
+	                       --extra-index-url https://download.pytorch.org/whl/cu121
+
+# Fix missing libnvinfer7
+USER root
+RUN ln -s /usr/lib/x86_64-linux-gnu/libnvinfer.so /usr/lib/x86_64-linux-gnu/libnvinfer.so.7 && \
+    ln -s /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.7
+
+USER appuser
+COPY --chown=appuser . .
+
+RUN sed -i 's/import library.huggingface_util/# import library.huggingface_util/g' train_network.py && \
+    sed -i 's/import library.huggingface_util/# import library.huggingface_util/g' library/train_util.py
+
+STOPSIGNAL SIGINT
+ENV LD_PRELOAD=libtcmalloc.so
+CMD . ./venv/bin/activate && python3 "./kohya_gui.py" ${CLI_ARGS} --listen 0.0.0.0 --server_port 7860

README.md

@@ -2,14 +2,13 @@
 
 This repository provides a Windows-focused Gradio GUI for [Kohya's Stable Diffusion trainers](https://github.com/kohya-ss/sd-scripts). The GUI allows you to set the training parameters and generate and run the required CLI commands to train the model.
 
-If you run on Linux and would like to use the GUI, there is now a port of it as a docker container. You can find the project [here](https://github.com/P2Enjoy/kohya_ss-docker).
-
 ### Table of Contents
 
 - [Tutorials](#tutorials)
 - [Required Dependencies](#required-dependencies)
   - [Linux/macOS](#linux-and-macos-dependencies)
 - [Installation](#installation)
+    - [Docker](#docker)
     - [Linux/macOS](#linux-and-macos)
       - [Default Install Locations](#install-location)
     - [Windows](#windows)
@@ -61,10 +60,33 @@ These dependencies are taken care of via `setup.sh` in the installation section.
 ### Runpod
 Follow the instructions found in this discussion: https://github.com/bmaltais/kohya_ss/discussions/379
 
+### Docker
+Docker is supported on Windows and Linux distributions. However this method currently only supports Nvidia GPUs. 
+Run the following commands in your OS shell after installing [git](https://git-scm.com/download/) and [docker](https://www.docker.com/products/docker-desktop/):
+```bash
+git clone https://github.com/bmaltais/kohya_ss.git
+cd kohya_ss
+docker compose up --build
+```
+
+This will take a while (up to 20 minutes) on the first run.
+
+The following limitations apply:
+* All training data must be added to the `dataset` subdirectory, the docker container cannot access any other files
+* The file picker does not work
+  * Cannot select folders, folder path must be set manually like e.g. /dataset/my_lora/img
+  * Cannot select config file, it must be loaded via path instead like e.g. /dataset/my_config.json  
+* Dialogs do not work
+  * Make sure your file names are unique as this happens when asking if an existing file should be overridden
+* No auto-update support. Must run update scripts outside docker manually and then rebuild with `docker compose build`.
+
+
+If you run on Linux, there is an alternative docker container port with less limitations. You can find the project [here](https://github.com/P2Enjoy/kohya_ss-docker).
+
 ### Linux and macOS
 In the terminal, run
 
-```
+```bash
 git clone https://github.com/bmaltais/kohya_ss.git
 cd kohya_ss
 # May need to chmod +x ./setup.sh if you're on a machine with stricter security.

docker-compose.yaml

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+version: "3.8"
+services:
+  kohya-ss-gui:
+    container_name: kohya-ss-gui
+    image: kohya-ss-gui:latest
+    build:
+      context: .
+    ports:
+      - 127.0.0.1:7860:7860
+    tty: true
+    ipc: host
+    environment:
+      CLI_ARGS: ""
+      SAFETENSORS_FAST_GPU: 1
+    tmpfs:
+      - /tmp      
+    volumes:
+      - ./dataset:/dataset
+      - ./.cache/user:/home/appuser/.cache
+      - ./.cache/triton:/home/appuser/.triton    
+      - ./.cache/config:/app/appuser/.config
+      - ./.cache/nv:/home/appuser/.nv 
+      - ./.cache/keras:/home/appuser/.keras      
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [gpu]