kohya_ss/Dockerfile at master · bmaltais/kohya_ss · GitHub

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# syntax=docker/dockerfile:1
ARG UID=1000
ARG VERSION=EDGE
ARG RELEASE=0

########################################
# Base stage
########################################
FROM docker.io/library/python:3.11-slim-bookworm AS base

# RUN mount cache for multi-arch: https://github.com/docker/buildx/issues/549#issuecomment-1788297892
ARG TARGETARCH
ARG TARGETVARIANT

WORKDIR /tmp

ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

# Install CUDA partially
# https://docs.nvidia.com/cuda/cuda-installation-guide-linux/#debian
# Installing the complete CUDA Toolkit system-wide usually adds around 8GB to the image size.
# Since most CUDA packages already installed through pip, there's no need to download the entire toolkit.
# Therefore, we opt to install only the essential libraries.
# Here is the package list for your reference: https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64

ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /tmp/cuda-keyring_x86_64.deb
RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \
    --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \
    dpkg -i cuda-keyring_x86_64.deb && \
    rm -f cuda-keyring_x86_64.deb && \
    apt-get update && \
    apt-get install -y --no-install-recommends \
    # !If you experience any related issues, replace the following line with `cuda-12-8` to obtain the complete CUDA package.
    cuda-nvcc-12-8

ENV PATH="/usr/local/cuda/bin${PATH:+:${PATH}}"
ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64
ENV CUDA_VERSION=12.8
ENV NVIDIA_REQUIRE_CUDA=cuda>=12.8
ENV CUDA_HOME=/usr/local/cuda

########################################
# Build stage
########################################
FROM base AS build

# RUN mount cache for multi-arch: https://github.com/docker/buildx/issues/549#issuecomment-1788297892
ARG TARGETARCH
ARG TARGETVARIANT

WORKDIR /app

# Install uv
COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/

ENV UV_PROJECT_ENVIRONMENT=/venv
ENV VIRTUAL_ENV=/venv
ENV UV_LINK_MODE=copy
ENV UV_PYTHON_DOWNLOADS=0
ENV UV_INDEX=https://download.pytorch.org/whl/cu128

# Install build dependencies
RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \
    --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \
    apt-get update && apt-get upgrade -y && \
    apt-get install -y --no-install-recommends python3-launchpadlib git curl

# Install big dependencies separately for layer caching
# !Please note that the version restrictions should be the same as pyproject.toml
# No packages listed should be removed in the next `uv sync` command
# If this happens, please update the version restrictions or update the uv.lock file
RUN --mount=type=cache,id=uv-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/root/.cache/uv \
    uv venv --system-site-packages /venv && \
    uv pip install --no-deps \
    # torch (1.0GiB)
    torch==2.7.0+cu128 \
    # triton (149.3MiB)
    triton>=3.1.0 \
    # tensorflow (615.0MiB)
    tensorflow>=2.16.1 \
    # onnxruntime-gpu (215.7MiB)
    onnxruntime-gpu==1.19.2

# Install dependencies
RUN --mount=type=cache,id=uv-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/root/.cache/uv \
    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
    --mount=type=bind,source=uv.lock,target=uv.lock \
    --mount=type=bind,source=sd-scripts,target=sd-scripts,rw \
    uv sync --frozen --no-dev --no-install-project --no-editable

# Replace pillow with pillow-simd (Only for x86)
ARG TARGETPLATFORM
RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \
    --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \
    if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
    apt-get update && apt-get install -y --no-install-recommends zlib1g-dev libjpeg62-turbo-dev build-essential && \
    uv pip uninstall pillow && \
    CC="cc -mavx2" uv pip install pillow-simd; \
    fi

########################################
# Final stage
########################################
FROM base AS final

ARG TARGETARCH
ARG TARGETVARIANT

WORKDIR /tmp

# Install runtime dependencies
RUN --mount=type=cache,id=apt-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/cache/apt \
    --mount=type=cache,id=aptlists-$TARGETARCH$TARGETVARIANT,sharing=locked,target=/var/lib/apt/lists \
    apt-get update && apt-get upgrade -y && \
    apt-get install -y --no-install-recommends libgl1 libglib2.0-0 libjpeg62 libtcl8.6 libtk8.6 libgoogle-perftools-dev dumb-init

# Fix missing libnvinfer7
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

# Create user
ARG UID
RUN groupadd -g $UID $UID && \
    useradd -l -u $UID -g $UID -m -s /bin/sh -N $UID

# Create directories with correct permissions
RUN install -d -m 775 -o $UID -g 0 /dataset && \
    install -d -m 775 -o $UID -g 0 /licenses && \
    install -d -m 775 -o $UID -g 0 /app && \
    install -d -m 775 -o $UID -g 0 /venv

# Copy licenses (OpenShift Policy)
COPY --link --chmod=775 LICENSE.md /licenses/LICENSE.md

# Copy dependencies and code (and support arbitrary uid for OpenShift best practice)
COPY --link --chown=$UID:0 --chmod=775 --from=build /venv /venv
COPY --link --chown=$UID:0 --chmod=775 . /app

ENV PATH="/venv/bin${PATH:+:${PATH}}"
ENV PYTHONPATH="/venv/lib/python3.11/site-packages"

ENV LD_LIBRARY_PATH="/venv/lib/python3.11/site-packages/nvidia/cudnn/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
ENV LD_PRELOAD=libtcmalloc.so
ENV PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

# Rich logging
# https://rich.readthedocs.io/en/stable/console.html#interactive-mode
ENV FORCE_COLOR="true"
ENV COLUMNS="100"

WORKDIR /app

VOLUME [ "/dataset" ]

# 7860: Kohya GUI
EXPOSE 7860

USER $UID

STOPSIGNAL SIGINT

# Use dumb-init as PID 1 to handle signals properly
ENTRYPOINT ["dumb-init", "--"]
CMD ["python3", "kohya_gui.py", "--listen", "0.0.0.0", "--server_port", "7860", "--headless", "--noverify"]

ARG VERSION
ARG RELEASE
LABEL name="bmaltais/kohya_ss" \
    vendor="bmaltais" \
    maintainer="bmaltais" \
    # Dockerfile source repository
    url="https://github.com/bmaltais/kohya_ss" \
    version=${VERSION} \
    # This should be a number, incremented with each change
    release=${RELEASE} \
    io.k8s.display-name="kohya_ss" \
    summary="Kohya's GUI: This repository provides a Gradio GUI for Kohya's Stable Diffusion trainers(https://github.com/kohya-ss/sd-scripts)." \
    description="The GUI allows you to set the training parameters and generate and run the required CLI commands to train the model. This is the docker image for Kohya's GUI. For more information about this tool, please visit the following website: https://github.com/bmaltais/kohya_ss."