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clean/consolidate dockerfile and change *.py to sed commands

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tako 2024-03-23 17:10:04 -04:00
parent 015c2ec829
commit 74cea377af
10 changed files with 1788 additions and 50 deletions
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1
.gitignore vendored
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/.devcontainer
/docker-compose.override.yml
.DS_Store
# VSCode specific
*.code-workspace

8
docker-compose.yml
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@ -71,3 +71,11 @@ services:
deploy: {}
environment:
- CLI_ARGS=--cpu
auto-m1:
<<: *automatic
profiles: ["auto-m1"]
deploy: {}
environment:
- CLI_ARGS=--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate

257
services/AUTOMATIC1111/Dockerfile
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@ -1,69 +1,226 @@
FROM alpine/git:2.36.2 as download
COPY clone.sh /clone.sh
# docker build -t sdwui:test -f Dockercleaned .
# docker run -it -p 7860:7861 sdwui:test
# FROM alpine/git:2.36.2 as download
# COPY clone.sh /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh
RUN . /clone.sh stable-diffusion-stability-ai https://github.com/Stability-AI/stablediffusion.git cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf \
&& rm -rf assets data/**/*.png data/**/*.jpg data/**/*.gif
# # RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh stable-diffusion-stability-ai https://github.com/Stability-AI/stablediffusion.git cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf \
# # && rm -rf assets data/**/*.png data/**/*.jpg data/**/*.gif
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh stable-diffusion-stability-ai https://github.com/Stability-AI/stablediffusion.git cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf
RUN . /clone.sh CodeFormer https://github.com/sczhou/CodeFormer.git c5b4593074ba6214284d6acd5f1719b6c5d739af \
&& rm -rf assets inputs
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh CodeFormer https://github.com/sczhou/CodeFormer.git c5b4593074ba6214284d6acd5f1719b6c5d739af \
# && rm -rf assets inputs
RUN . /clone.sh BLIP https://github.com/salesforce/BLIP.git 48211a1594f1321b00f14c9f7a5b4813144b2fb9
RUN . /clone.sh k-diffusion https://github.com/crowsonkb/k-diffusion.git ab527a9a6d347f364e3d185ba6d714e22d80cb3c
RUN . /clone.sh clip-interrogator https://github.com/pharmapsychotic/clip-interrogator 2cf03aaf6e704197fd0dae7c7f96aa59cf1b11c9
RUN . /clone.sh generative-models https://github.com/Stability-AI/generative-models 45c443b316737a4ab6e40413d7794a7f5657c19f
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh BLIP https://github.com/salesforce/BLIP.git 48211a1594f1321b00f14c9f7a5b4813144b2fb9
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh k-diffusion https://github.com/crowsonkb/k-diffusion.git ab527a9a6d347f364e3d185ba6d714e22d80cb3c
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh clip-interrogator https://github.com/pharmapsychotic/clip-interrogator 2cf03aaf6e704197fd0dae7c7f96aa59cf1b11c9
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh generative-models https://github.com/Stability-AI/generative-models 45c443b316737a4ab6e40413d7794a7f5657c19f
###################################################################################################################################################
###################################################################################################################################################
###################################################################################################################################################
FROM ubuntu:22.04
FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime
# Set env vars
ARG GRADIO_SERVER_PORT=7860
ENV GRADIO_SERVER_PORT=${GRADIO_SERVER_PORT}
ENV DEBIAN_FRONTEND=noninteractive PIP_PREFER_BINARY=1
# These settings prevent a timezone prompt when Python installs
ENV TZ=US/Pacific \
DEBIAN_FRONTEND=noninteractive
RUN --mount=type=cache,target=/var/cache/apt \
apt-get update && \
# we need those
apt-get install -y fonts-dejavu-core rsync git jq moreutils aria2 \
# extensions needs those
ffmpeg libglfw3-dev libgles2-mesa-dev pkg-config libcairo2 libcairo2-dev build-essential
RUN echo "#################################################"
RUN echo "Get the latest APT packages"
RUN echo "apt-get update"
RUN apt-get update
# Install AUTOMATIC1111 pre-requisites
RUN apt-get install -y \
cmake \
rustc \
git-all \
wget \
apt-utils \
jq \
pip
WORKDIR /
RUN --mount=type=cache,target=/root/.cache/pip \
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git && \
cd stable-diffusion-webui && \
git reset --hard cf2772fab0af5573da775e7437e6acdca424f26e && \
RUN apt -y autoremove && apt autoclean
# Install Python pre-requisites, including Python 3.x
# Google perftools includes TCMalloc, which helps with CPU memory usage
RUN apt-get install -y \
software-properties-common \
python3 \
python3-pip \
python3-ipykernel \
libopencv-dev \
python3-opencv \
python3.10-venv \
google-perftools \
sudo
RUN apt -y autoremove && apt autoclean
# Configure git
RUN git config --global user.name "Some One" &&\
git config --global user.email some.one@some.one &&\
git config --global init.defaultBranch main
RUN sudo apt purge gcc -y
RUN sudo apt purge libomp-dev -y
RUN export USE_OPENMP=1
ENV USE_OPENMP=1
RUN sudo apt-get install gcc libomp-dev -y
# Check versions
RUN echo |cpp -fopenmp -dM |grep -i open
RUN gcc --version
RUN echo "deb http://public.dhe.ibm.com/software/server/POWER/Linux/xl-compiler/eval/ppc64le/ubuntu/ trusty main" | sudo tee /etc/apt/sources.list.d/ibm-xl-compiler-eval.list
RUN sudo apt-get update
RUN "/usr/bin/python3" -m pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url https://download.pytorch.org/whl/cu121
# RUN mkdir -p /home/stable_diffusion_webui/stable-diffusion-webui
# WORKDIR /home/stable_diffusion_webui
#
#
#
# REPLACE COPY WITH GIT CLONE
# https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/bef51aed032c0aaa5cfd80445bc4cf0d85b408b5
RUN mkdir -p /home/stable_diffusion_webui
WORKDIR /home/stable_diffusion_webui
# COPY clone.sh /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh
# RUN . /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh stable-diffusion-webui https://github.com/AUTOMATIC1111/stable-diffusion-webui.git bef51aed032c0aaa5cfd80445bc4cf0d85b408b5
# RUN git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git /home/stable_diffusion_webui/stable-diffusion-webui
# WORKDIR /home/stable_diffusion_webui/stable-diffusion-webui
# RUN git checkout bef51aed032c0aaa5cfd80445bc4cf0d85b408b5
# WORKDIR /home/stable_diffusion_webui
# Using their way
# RUN --mount=type=cache,target=/root/.cache/pip \
RUN git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git /home/stable_diffusion_webui/stable-diffusion-webui && \
cd /home/stable_diffusion_webui/stable-diffusion-webui && \
git reset --hard bef51aed032c0aaa5cfd80445bc4cf0d85b408b5 && \
pip install -r requirements_versions.txt
# cd -
RUN "/usr/bin/python3" -m pip install -r /home/stable_diffusion_webui/stable-diffusion-webui/requirements_versions.txt
WORKDIR /home/stable_diffusion_webui
ENV ROOT=/stable-diffusion-webui
COPY --from=download /repositories/ ${ROOT}/repositories/
RUN mkdir ${ROOT}/interrogate && cp ${ROOT}/repositories/clip-interrogator/clip_interrogator/data/* ${ROOT}/interrogate
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r ${ROOT}/repositories/CodeFormer/requirements.txt
# Modify code from AUTOMATIC1111 repo as needed for Mac M1
COPY paths_internal.py /home/stable_diffusion_webui/stable-diffusion-webui/modules/paths_internal.py
COPY sd_models.py /home/stable_diffusion_webui/stable-diffusion-webui/modules/sd_models.py
COPY webui-macos-env.sh /home/stable_diffusion_webui/stable-diffusion-webui/webui-macos-env.sh
COPY webui-user.sh /home/stable_diffusion_webui/stable-diffusion-webui/webui-user.sh
RUN --mount=type=cache,target=/root/.cache/pip \
pip install pyngrok xformers==0.0.23.post1 \
git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379 \
git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1 \
git+https://github.com/mlfoundations/open_clip.git@v2.20.0
# IS THIS NEEDED?
COPY webui.sh /home/stable_diffusion_webui/stable-diffusion-webui/webui.sh
# there seems to be a memory leak (or maybe just memory not being freed fast enough) that is fixed by this version of malloc
# maybe move this up to the dependencies list.
RUN apt-get -y install libgoogle-perftools-dev && apt-get clean
ENV LD_PRELOAD=libtcmalloc.so
RUN chmod 777 -R /home/stable_diffusion_webui/stable-diffusion-webui/
COPY . /docker
RUN \
# mv ${ROOT}/style.css ${ROOT}/user.css && \
# one of the ugliest hacks I ever wrote \
sed -i 's/in_app_dir = .*/in_app_dir = True/g' /opt/conda/lib/python3.10/site-packages/gradio/routes.py && \
git config --global --add safe.directory '*'
WORKDIR ${ROOT}
ENV NVIDIA_VISIBLE_DEVICES=all
ENV CLI_ARGS=""
EXPOSE 7860
ENTRYPOINT ["/docker/entrypoint.sh"]
CMD python -u webui.py --listen --port 7860 ${CLI_ARGS}
RUN export OPENBLAS_NUM_THREADS=1
WORKDIR /home/stable_diffusion_webui/stable-diffusion-webui
RUN git config --global --add safe.directory "*"
# RUN useradd -s /bin/bash -d /home/sdwui/ -m -G sudo sdwui
# USER sdwui
# RUN git config --global --add safe.directory "*"
# RUN ./webui.sh
# RUN source /home/stable_diffusion_webui/stable-diffusion-webui/webui-macos-env.sh
# RUN source source /home/stable_diffusion_webui/stable-diffusion-webui/webui-user.sh
# RUN "/usr/bin/python3" -m pip install -r requirements_versions.txt
# # Need to be after `webui.sh` installs reqs like gradio
# RUN curl -LS https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_aarch64 -o frpc_linux_aarch64_v0.2
# # ENTRYPOINT [ "/bin/bash" ]
# # ENV python=/usr/bin/python3
# # RUN site_pkg_loc=`python -m site | grep 'site-packages' | head -1 | awk -F"'" '{print $2}' -`; mv frpc_linux_aarch64_v0.2 `echo $site_pkg_loc`/gradio/frpc_linux_aarch64_v0.2; chmod +x `echo $site_pkg_loc`/gradio/frpc_linux_aarch64_v0.2
# RUN mv frpc_linux_aarch64_v0.2 /home/sdwui/.local/lib/python3.10/site-packages/gradio/.
# RUN mv frpc_linux_aarch64_v0.2 /home/sdwui/.local/lib/python3.10/site-packages/gradio/.
# RUN chmod +x /home/sdwui/.local/lib/python3.10/site-packages/gradio/frpc_linux_aarch64_v0.2
####################################################################################################
RUN "/usr/bin/python3" -m pip install gradio==3.41.2 && \
curl -LS https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_aarch64 -o frpc_linux_aarch64_v0.2 && \
chmod +x frpc_linux_aarch64_v0.2
##################################################
RUN if [ -d /usr/local/lib/python3.10/dist-packages/gradio/ ]; then scp frpc_linux_aarch64_v0.2 /usr/local/lib/python3.10/dist-packages/gradio/.; fi
RUN if [ -d /usr/lib/python3/dist-packages/gradio ]; then scp frpc_linux_aarch64_v0.2 /usr/lib/python3.10/dist-packages/gradio/.; fi
RUN if [ -d /usr/lib/python3.10/dist-packages/gradio ]; then scp frpc_linux_aarch64_v0.2 /usr/lib/python3.10/dist-packages/gradio/.; fi
RUN if [ -d /home/sdwui/.local/lib/python3.10/site-packages/gradio ]; then scp frpc_linux_aarch64_v0.2 /home/sdwui/.local/lib/python3.10/site-packages/gradio/.; fi
##################################################
RUN if [ -d /home/sdwui/.local/lib/python3.10/site-packages/gradio ]; then \
GRADIO_DIR_PATH=/home/sdwui/.local/lib/python3.10/site-packages/gradio \
else \
GRADIO_DIR_PATH=/usr/lib/python3/dist-packages/gradio; fi && \
mv frpc_linux_aarch64_v0.2 ${GRADIO_DIR_PATH}/frpc_linux_aarch64_v0.2 && \
chmod +x ${GRADIO_DIR_PATH}/frpc_linux_aarch64_v0.2
####################################################################################################
# RUN mv /home/stable_diffusion_webui/stable-diffusion-webui /stable-diffusion-webui
# WORKDIR /stable-diffusion-webui
# RUN useradd -s /bin/bash -d /home/sdwui/ -m -G sudo sdwui
# USER sdwui
# RUN git config --global --add safe.directory "*"
# COPY entrypoint.sh /home/stable_diffusion_webui/entrypoint.sh
RUN export USE_OPENMP=1
ENV USE_OPENMP=1
RUN export OPENBLAS_NUM_THREADS=1
ENV OPENBLAS_NUM_THREADS=1
RUN mkdir -p /home/stable_diffusion_webui/stable-diffusion-webui/repositories
COPY clone.sh /home/stable_diffusion_webui/stable-diffusion-webui/.
WORKDIR /home/stable_diffusion_webui/stable-diffusion-webui
RUN chmod +x /home/stable_diffusion_webui/stable-diffusion-webui/clone.sh
# RUN git clone https://github.com/Stability-AI/stablediffusion.git \
# && cd stablediffusion && \
# && git reset --hard cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf \
# && rm -rf assets data/**/*.png data/**/*.jpg data/**/*.gif .git \
# && cd .. \
# && mkdir /home/stable_diffusion_webui/stable-diffusion-webui/repositories \
# && mv stablediffusion /home/stable_diffusion_webui/stable-diffusion-webui/repositories/stable-diffusion-stability-ai
RUN ./clone.sh CodeFormer https://github.com/sczhou/CodeFormer.git c5b4593074ba6214284d6acd5f1719b6c5d739af \
&& rm -rf assets inputs
RUN ./clone.sh stable-diffusion-stability-ai https://github.com/Stability-AI/stablediffusion.git cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf
RUN ./clone.sh BLIP https://github.com/salesforce/BLIP.git 48211a1594f1321b00f14c9f7a5b4813144b2fb9
RUN ./clone.sh k-diffusion https://github.com/crowsonkb/k-diffusion.git ab527a9a6d347f364e3d185ba6d714e22d80cb3c
RUN ./clone.sh clip-interrogator https://github.com/pharmapsychotic/clip-interrogator 2cf03aaf6e704197fd0dae7c7f96aa59cf1b11c9
RUN ./clone.sh generative-models https://github.com/Stability-AI/generative-models 45c443b316737a4ab6e40413d7794a7f5657c19f
RUN ./clone.sh stable-diffusion-webui-assets https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets.git 6f7db241d2f8ba7457bac5ca9753331f0c266917
RUN mv /repositories/* /home/stable_diffusion_webui/stable-diffusion-webui/repositories/.
RUN "/usr/bin/python3" -m pip install clip
############################################################################################################################################
##############
### REMOVE ###
##############
# COPY sd-v1-4.ckpt /home/stable_diffusion_webui/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt
############################################################################################################################################
# RUN export COMMANDLINE_ARGS="--skip-torch-cuda-test --opt-sdp-attention --precision full --no-half --upcast-sampling --no-half-vae --use-cpu interrogate"
# ENV COMMANDLINE_ARGS="--skip-torch-cuda-test --opt-sdp-attention --precision full --no-half --upcast-sampling --no-half-vae --use-cpu interrogate"
# CMD /usr/bin/python3 -u webui.py --listen --port 7860 ${COMMANDLINE_ARGS}
CMD /usr/bin/python3 -u webui.py --listen --port 7860
# ENTRYPOINT [ "/bin/bash" ]

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services/AUTOMATIC1111/Dockerfile.theirs Normal file
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FROM alpine/git:2.36.2 as download
COPY clone.sh /clone.sh
RUN . /clone.sh stable-diffusion-stability-ai https://github.com/Stability-AI/stablediffusion.git cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf \
&& rm -rf assets data/**/*.png data/**/*.jpg data/**/*.gif
RUN . /clone.sh CodeFormer https://github.com/sczhou/CodeFormer.git c5b4593074ba6214284d6acd5f1719b6c5d739af \
&& rm -rf assets inputs
RUN . /clone.sh BLIP https://github.com/salesforce/BLIP.git 48211a1594f1321b00f14c9f7a5b4813144b2fb9
RUN . /clone.sh k-diffusion https://github.com/crowsonkb/k-diffusion.git ab527a9a6d347f364e3d185ba6d714e22d80cb3c
RUN . /clone.sh clip-interrogator https://github.com/pharmapsychotic/clip-interrogator 2cf03aaf6e704197fd0dae7c7f96aa59cf1b11c9
RUN . /clone.sh generative-models https://github.com/Stability-AI/generative-models 45c443b316737a4ab6e40413d7794a7f5657c19f
FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime
ENV DEBIAN_FRONTEND=noninteractive PIP_PREFER_BINARY=1
RUN --mount=type=cache,target=/var/cache/apt \
apt-get update && \
# we need those
apt-get install -y fonts-dejavu-core rsync git jq moreutils aria2 \
# extensions needs those
ffmpeg libglfw3-dev libgles2-mesa-dev pkg-config libcairo2 libcairo2-dev build-essential
WORKDIR /
RUN --mount=type=cache,target=/root/.cache/pip \
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git && \
cd stable-diffusion-webui && \
git reset --hard cf2772fab0af5573da775e7437e6acdca424f26e && \
pip install -r requirements_versions.txt
ENV ROOT=/stable-diffusion-webui
COPY --from=download /repositories/ ${ROOT}/repositories/
RUN mkdir ${ROOT}/interrogate && cp ${ROOT}/repositories/clip-interrogator/clip_interrogator/data/* ${ROOT}/interrogate
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r ${ROOT}/repositories/CodeFormer/requirements.txt
RUN --mount=type=cache,target=/root/.cache/pip \
pip install pyngrok xformers==0.0.23.post1 \
git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379 \
git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1 \
git+https://github.com/mlfoundations/open_clip.git@v2.20.0
# there seems to be a memory leak (or maybe just memory not being freed fast enough) that is fixed by this version of malloc
# maybe move this up to the dependencies list.
RUN apt-get -y install libgoogle-perftools-dev && apt-get clean
ENV LD_PRELOAD=libtcmalloc.so
COPY . /docker
RUN \
# mv ${ROOT}/style.css ${ROOT}/user.css && \
# one of the ugliest hacks I ever wrote \
sed -i 's/in_app_dir = .*/in_app_dir = True/g' /opt/conda/lib/python3.10/site-packages/gradio/routes.py && \
git config --global --add safe.directory '*'
WORKDIR ${ROOT}
ENV NVIDIA_VISIBLE_DEVICES=all
ENV CLI_ARGS=""
EXPOSE 7860
ENTRYPOINT ["/docker/entrypoint.sh"]
CMD python -u webui.py --listen --port 7860 ${CLI_ARGS}

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services/AUTOMATIC1111/paths_internal.py Normal file
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"""this module defines internal paths used by program and is safe to import before dependencies are installed in launch.py"""
import argparse
import os
import sys
import shlex
from pathlib import Path
normalized_filepath = lambda filepath: str(Path(filepath).absolute())
# commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
commandline_args = os.environ.get('COMMANDLINE_ARGS', "--skip-torch-cuda-test --opt-sdp-attention --precision full --no-half")
sys.argv += shlex.split(commandline_args)
cwd = os.getcwd()
modules_path = os.path.dirname(os.path.realpath(__file__))
script_path = os.path.dirname(modules_path)
sd_configs_path = os.path.join(script_path, "configs")
sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
sd_model_file = os.path.join(script_path, 'model.ckpt')
default_sd_model_file = sd_model_file
# Parse the --data-dir flag first so we can use it as a base for our other argument default values
parser_pre = argparse.ArgumentParser(add_help=False)
parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
cmd_opts_pre = parser_pre.parse_known_args()[0]
data_path = cmd_opts_pre.data_dir
models_path = os.path.join(data_path, "models")
extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
config_states_dir = os.path.join(script_path, "config_states")
default_output_dir = os.path.join(data_path, "output")
roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')

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services/AUTOMATIC1111/sd_models.py Normal file
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import collections
import os.path
import sys
import threading
import torch
import re
import safetensors.torch
from omegaconf import OmegaConf, ListConfig
from os import mkdir
from urllib import request
import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
from modules.shared import opts
import tomesd
import numpy as np
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
checkpoints_list = {}
checkpoint_aliases = {}
checkpoint_alisases = checkpoint_aliases # for compatibility with old name
checkpoints_loaded = collections.OrderedDict()
def replace_key(d, key, new_key, value):
keys = list(d.keys())
d[new_key] = value
if key not in keys:
return d
index = keys.index(key)
keys[index] = new_key
new_d = {k: d[k] for k in keys}
d.clear()
d.update(new_d)
return d
class CheckpointInfo:
def __init__(self, filename):
self.filename = filename
abspath = os.path.abspath(filename)
abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
name = abspath.replace(abs_ckpt_dir, '')
elif abspath.startswith(model_path):
name = abspath.replace(model_path, '')
else:
name = os.path.basename(filename)
if name.startswith("\\") or name.startswith("/"):
name = name[1:]
def read_metadata():
metadata = read_metadata_from_safetensors(filename)
self.modelspec_thumbnail = metadata.pop('modelspec.thumbnail', None)
return metadata
self.metadata = {}
if self.is_safetensors:
try:
self.metadata = cache.cached_data_for_file('safetensors-metadata', "checkpoint/" + name, filename, read_metadata)
except Exception as e:
errors.display(e, f"reading metadata for {filename}")
self.name = name
self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
self.hash = model_hash(filename)
self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
self.shorthash = self.sha256[0:10] if self.sha256 else None
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
if self.shorthash:
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
def register(self):
checkpoints_list[self.title] = self
for id in self.ids:
checkpoint_aliases[id] = self
def calculate_shorthash(self):
self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
if self.sha256 is None:
return
shorthash = self.sha256[0:10]
if self.shorthash == self.sha256[0:10]:
return self.shorthash
self.shorthash = shorthash
if self.shorthash not in self.ids:
self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
old_title = self.title
self.title = f'{self.name} [{self.shorthash}]'
self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
replace_key(checkpoints_list, old_title, self.title, self)
self.register()
return self.shorthash
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging, CLIPModel # noqa: F401
logging.set_verbosity_error()
except Exception:
pass
def setup_model():
"""called once at startup to do various one-time tasks related to SD models"""
os.makedirs(model_path, exist_ok=True)
enable_midas_autodownload()
patch_given_betas()
def checkpoint_tiles(use_short=False):
return [x.short_title if use_short else x.title for x in checkpoints_list.values()]
def list_models():
checkpoints_list.clear()
checkpoint_aliases.clear()
cmd_ckpt = shared.cmd_opts.ckpt
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
model_url = None
else:
# model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
# _download_name_="v1-5-pruned-emaonly.safetensors"
model_url = "https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt"
_download_name_ = "sd-v1-4.ckpt"
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name=_download_name_, ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
if os.path.exists(cmd_ckpt):
checkpoint_info = CheckpointInfo(cmd_ckpt)
checkpoint_info.register()
shared.opts.data['sd_model_checkpoint'] = checkpoint_info.title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
for filename in model_list:
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
def get_closet_checkpoint_match(search_string):
if not search_string:
return None
checkpoint_info = checkpoint_aliases.get(search_string, None)
if checkpoint_info is not None:
return checkpoint_info
found = sorted([info for info in checkpoints_list.values() if search_string in info.title], key=lambda x: len(x.title))
if found:
return found[0]
search_string_without_checksum = re.sub(re_strip_checksum, '', search_string)
found = sorted([info for info in checkpoints_list.values() if search_string_without_checksum in info.title], key=lambda x: len(x.title))
if found:
return found[0]
return None
def model_hash(filename):
"""old hash that only looks at a small part of the file and is prone to collisions"""
try:
with open(filename, "rb") as file:
import hashlib
m = hashlib.sha256()
file.seek(0x100000)
m.update(file.read(0x10000))
return m.hexdigest()[0:8]
except FileNotFoundError:
return 'NOFILE'
def select_checkpoint():
"""Raises `FileNotFoundError` if no checkpoints are found."""
model_checkpoint = shared.opts.sd_model_checkpoint
checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
if checkpoint_info is not None:
return checkpoint_info
if len(checkpoints_list) == 0:
error_message = "No checkpoints found. When searching for checkpoints, looked at:"
if shared.cmd_opts.ckpt is not None:
error_message += f"\n - file {os.path.abspath(shared.cmd_opts.ckpt)}"
error_message += f"\n - directory {model_path}"
if shared.cmd_opts.ckpt_dir is not None:
error_message += f"\n - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}"
error_message += "Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations."
raise FileNotFoundError(error_message)
checkpoint_info = next(iter(checkpoints_list.values()))
if model_checkpoint is not None:
print(f"Checkpoint {model_checkpoint} not found; loading fallback {checkpoint_info.title}", file=sys.stderr)
return checkpoint_info
checkpoint_dict_replacements_sd1 = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
}
checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
'conditioner.embedders.0.': 'cond_stage_model.',
}
def transform_checkpoint_dict_key(k, replacements):
for text, replacement in replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
return k
def get_state_dict_from_checkpoint(pl_sd):
pl_sd = pl_sd.pop("state_dict", pl_sd)
pl_sd.pop("state_dict", None)
is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
sd = {}
for k, v in pl_sd.items():
if is_sd2_turbo:
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
else:
new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
if new_key is not None:
sd[new_key] = v
pl_sd.clear()
pl_sd.update(sd)
return pl_sd
def read_metadata_from_safetensors(filename):
import json
with open(filename, mode="rb") as file:
metadata_len = file.read(8)
metadata_len = int.from_bytes(metadata_len, "little")
json_start = file.read(2)
assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
json_data = json_start + file.read(metadata_len-2)
json_obj = json.loads(json_data)
res = {}
for k, v in json_obj.get("__metadata__", {}).items():
res[k] = v
if isinstance(v, str) and v[0:1] == '{':
try:
res[k] = json.loads(v)
except Exception:
pass
return res
def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
_, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
if not shared.opts.disable_mmap_load_safetensors:
pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
else:
pl_sd = safetensors.torch.load(open(checkpoint_file, 'rb').read())
pl_sd = {k: v.to(device) for k, v in pl_sd.items()}
else:
pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
if print_global_state and "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
if pl_sd == None:
sd = {}
else:
sd = get_state_dict_from_checkpoint(pl_sd)
return sd
def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
if checkpoint_info in checkpoints_loaded:
# use checkpoint cache
print(f"Loading weights [{sd_model_hash}] from cache")
# move to end as latest
checkpoints_loaded.move_to_end(checkpoint_info)
return checkpoints_loaded[checkpoint_info]
print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
res = read_state_dict(checkpoint_info.filename)
timer.record("load weights from disk")
return res
class SkipWritingToConfig:
"""This context manager prevents load_model_weights from writing checkpoint name to the config when it loads weight."""
skip = False
previous = None
def __enter__(self):
self.previous = SkipWritingToConfig.skip
SkipWritingToConfig.skip = True
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
SkipWritingToConfig.skip = self.previous
def check_fp8(model):
if model is None:
return None
if devices.get_optimal_device_name() == "mps":
enable_fp8 = False
elif shared.opts.fp8_storage == "Enable":
enable_fp8 = True
elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL":
enable_fp8 = True
else:
enable_fp8 = False
return enable_fp8
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
if devices.fp8:
# prevent model to load state dict in fp8
model.half()
if not SkipWritingToConfig.skip:
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
if state_dict is None:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
model.is_sdxl = hasattr(model, 'conditioner')
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
model.is_sd1 = not model.is_sdxl and not model.is_sd2
model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
if model.is_sdxl:
sd_models_xl.extend_sdxl(model)
if model.is_ssd:
sd_hijack.model_hijack.convert_sdxl_to_ssd(model)
if shared.opts.sd_checkpoint_cache > 0:
# cache newly loaded model
checkpoints_loaded[checkpoint_info] = state_dict.copy()
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
del state_dict
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
timer.record("apply channels_last")
if shared.cmd_opts.no_half:
model.float()
model.alphas_cumprod_original = model.alphas_cumprod
devices.dtype_unet = torch.float32
timer.record("apply float()")
else:
vae = model.first_stage_model
depth_model = getattr(model, 'depth_model', None)
# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
if shared.cmd_opts.no_half_vae:
model.first_stage_model = None
# with --upcast-sampling, don't convert the depth model weights to float16
if shared.cmd_opts.upcast_sampling and depth_model:
model.depth_model = None
alphas_cumprod = model.alphas_cumprod
model.alphas_cumprod = None
model.half()
model.alphas_cumprod = alphas_cumprod
model.alphas_cumprod_original = alphas_cumprod
model.first_stage_model = vae
if depth_model:
model.depth_model = depth_model
devices.dtype_unet = torch.float16
timer.record("apply half()")
apply_alpha_schedule_override(model)
for module in model.modules():
if hasattr(module, 'fp16_weight'):
del module.fp16_weight
if hasattr(module, 'fp16_bias'):
del module.fp16_bias
if check_fp8(model):
devices.fp8 = True
first_stage = model.first_stage_model
model.first_stage_model = None
for module in model.modules():
if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
if shared.opts.cache_fp16_weight:
module.fp16_weight = module.weight.data.clone().cpu().half()
if module.bias is not None:
module.fp16_bias = module.bias.data.clone().cpu().half()
module.to(torch.float8_e4m3fn)
model.first_stage_model = first_stage
timer.record("apply fp8")
else:
devices.fp8 = False
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
model.first_stage_model.to(devices.dtype_vae)
timer.record("apply dtype to VAE")
# clean up cache if limit is reached
while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
checkpoints_loaded.popitem(last=False)
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_info.filename
model.sd_checkpoint_info = checkpoint_info
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
if hasattr(model, 'logvar'):
model.logvar = model.logvar.to(devices.device) # fix for training
sd_vae.delete_base_vae()
sd_vae.clear_loaded_vae()
vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
sd_vae.load_vae(model, vae_file, vae_source)
timer.record("load VAE")
def enable_midas_autodownload():
"""
Gives the ldm.modules.midas.api.load_model function automatic downloading.
When the 512-depth-ema model, and other future models like it, is loaded,
it calls midas.api.load_model to load the associated midas depth model.
This function applies a wrapper to download the model to the correct
location automatically.
"""
midas_path = os.path.join(paths.models_path, 'midas')
# stable-diffusion-stability-ai hard-codes the midas model path to
# a location that differs from where other scripts using this model look.
# HACK: Overriding the path here.
for k, v in midas.api.ISL_PATHS.items():
file_name = os.path.basename(v)
midas.api.ISL_PATHS[k] = os.path.join(midas_path, file_name)
midas_urls = {
"dpt_large": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
"dpt_hybrid": "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt",
"midas_v21": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21-f6b98070.pt",
"midas_v21_small": "https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/midas_v21_small-70d6b9c8.pt",
}
midas.api.load_model_inner = midas.api.load_model
def load_model_wrapper(model_type):
path = midas.api.ISL_PATHS[model_type]
if not os.path.exists(path):
if not os.path.exists(midas_path):
mkdir(midas_path)
print(f"Downloading midas model weights for {model_type} to {path}")
request.urlretrieve(midas_urls[model_type], path)
print(f"{model_type} downloaded")
return midas.api.load_model_inner(model_type)
midas.api.load_model = load_model_wrapper
def patch_given_betas():
import ldm.models.diffusion.ddpm
def patched_register_schedule(*args, **kwargs):
"""a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
if isinstance(args[1], ListConfig):
args = (args[0], np.array(args[1]), *args[2:])
original_register_schedule(*args, **kwargs)
original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
def repair_config(sd_config):
if not hasattr(sd_config.model.params, "use_ema"):
sd_config.model.params.use_ema = False
if hasattr(sd_config.model.params, 'unet_config'):
if shared.cmd_opts.no_half:
sd_config.model.params.unet_config.params.use_fp16 = False
elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
# For UnCLIP-L, override the hardcoded karlo directory
if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
karlo_path = os.path.join(paths.models_path, 'karlo')
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
def rescale_zero_terminal_snr_abar(alphas_cumprod):
alphas_bar_sqrt = alphas_cumprod.sqrt()
# Store old values.
alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
# Shift so the last timestep is zero.
alphas_bar_sqrt -= (alphas_bar_sqrt_T)
# Scale so the first timestep is back to the old value.
alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
# Convert alphas_bar_sqrt to betas
alphas_bar = alphas_bar_sqrt ** 2 # Revert sqrt
alphas_bar[-1] = 4.8973451890853435e-08
return alphas_bar
def apply_alpha_schedule_override(sd_model, p=None):
"""
Applies an override to the alpha schedule of the model according to settings.
- downcasts the alpha schedule to half precision
- rescales the alpha schedule to have zero terminal SNR
"""
if not hasattr(sd_model, 'alphas_cumprod') or not hasattr(sd_model, 'alphas_cumprod_original'):
return
sd_model.alphas_cumprod = sd_model.alphas_cumprod_original.to(shared.device)
if opts.use_downcasted_alpha_bar:
if p is not None:
p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
if opts.sd_noise_schedule == "Zero Terminal SNR":
if p is not None:
p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
sdxl_refiner_clip_weight = 'conditioner.embedders.0.model.ln_final.weight'
class SdModelData:
def __init__(self):
self.sd_model = None
self.loaded_sd_models = []
self.was_loaded_at_least_once = False
self.lock = threading.Lock()
def get_sd_model(self):
if self.was_loaded_at_least_once:
return self.sd_model
if self.sd_model is None:
with self.lock:
if self.sd_model is not None or self.was_loaded_at_least_once:
return self.sd_model
try:
load_model()
except Exception as e:
errors.display(e, "loading stable diffusion model", full_traceback=True)
print("", file=sys.stderr)
print("Stable diffusion model failed to load", file=sys.stderr)
self.sd_model = None
return self.sd_model
def set_sd_model(self, v, already_loaded=False):
self.sd_model = v
if already_loaded:
sd_vae.base_vae = getattr(v, "base_vae", None)
sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
sd_vae.checkpoint_info = v.sd_checkpoint_info
try:
self.loaded_sd_models.remove(v)
except ValueError:
pass
if v is not None:
self.loaded_sd_models.insert(0, v)
model_data = SdModelData()
def get_empty_cond(sd_model):
p = processing.StableDiffusionProcessingTxt2Img()
extra_networks.activate(p, {})
if hasattr(sd_model, 'conditioner'):
d = sd_model.get_learned_conditioning([""])
return d['crossattn']
else:
return sd_model.cond_stage_model([""])
def send_model_to_cpu(m):
if m.lowvram:
lowvram.send_everything_to_cpu()
else:
m.to(devices.cpu)
devices.torch_gc()
def model_target_device(m):
if lowvram.is_needed(m):
return devices.cpu
else:
return devices.device
def send_model_to_device(m):
lowvram.apply(m)
if not m.lowvram:
m.to(shared.device)
def send_model_to_trash(m):
m.to(device="meta")
devices.torch_gc()
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
from modules import sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
timer = Timer()
if model_data.sd_model:
send_model_to_trash(model_data.sd_model)
model_data.sd_model = None
devices.torch_gc()
timer.record("unload existing model")
if already_loaded_state_dict is not None:
state_dict = already_loaded_state_dict
else:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
clip_is_included_into_sd = any(x for x in [sd1_clip_weight, sd2_clip_weight, sdxl_clip_weight, sdxl_refiner_clip_weight] if x in state_dict)
timer.record("find config")
sd_config = OmegaConf.load(checkpoint_config)
repair_config(sd_config)
timer.record("load config")
print(f"Creating model from config: {checkpoint_config}")
sd_model = None
try:
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
with sd_disable_initialization.InitializeOnMeta():
sd_model = instantiate_from_config(sd_config.model)
except Exception as e:
errors.display(e, "creating model quickly", full_traceback=True)
if sd_model is None:
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
with sd_disable_initialization.InitializeOnMeta():
sd_model = instantiate_from_config(sd_config.model)
sd_model.used_config = checkpoint_config
timer.record("create model")
if shared.cmd_opts.no_half:
weight_dtype_conversion = None
else:
weight_dtype_conversion = {
'first_stage_model': None,
'alphas_cumprod': None,
'': torch.float16,
}
with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
timer.record("load weights from state dict")
send_model_to_device(sd_model)
timer.record("move model to device")
sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
sd_model.eval()
model_data.set_sd_model(sd_model)
model_data.was_loaded_at_least_once = True
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
timer.record("load textual inversion embeddings")
script_callbacks.model_loaded_callback(sd_model)
timer.record("scripts callbacks")
with devices.autocast(), torch.no_grad():
sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
timer.record("calculate empty prompt")
print(f"Model loaded in {timer.summary()}.")
return sd_model
def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
"""
Checks if the desired checkpoint from checkpoint_info is not already loaded in model_data.loaded_sd_models.
If it is loaded, returns that (moving it to GPU if necessary, and moving the currently loadded model to CPU if necessary).
If not, returns the model that can be used to load weights from checkpoint_info's file.
If no such model exists, returns None.
Additionaly deletes loaded models that are over the limit set in settings (sd_checkpoints_limit).
"""
already_loaded = None
for i in reversed(range(len(model_data.loaded_sd_models))):
loaded_model = model_data.loaded_sd_models[i]
if loaded_model.sd_checkpoint_info.filename == checkpoint_info.filename:
already_loaded = loaded_model
continue
if len(model_data.loaded_sd_models) > shared.opts.sd_checkpoints_limit > 0:
print(f"Unloading model {len(model_data.loaded_sd_models)} over the limit of {shared.opts.sd_checkpoints_limit}: {loaded_model.sd_checkpoint_info.title}")
model_data.loaded_sd_models.pop()
send_model_to_trash(loaded_model)
timer.record("send model to trash")
if shared.opts.sd_checkpoints_keep_in_cpu:
send_model_to_cpu(sd_model)
timer.record("send model to cpu")
if already_loaded is not None:
send_model_to_device(already_loaded)
timer.record("send model to device")
model_data.set_sd_model(already_loaded, already_loaded=True)
if not SkipWritingToConfig.skip:
shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title
shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256
print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
sd_vae.reload_vae_weights(already_loaded)
return model_data.sd_model
elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
model_data.sd_model = None
load_model(checkpoint_info)
return model_data.sd_model
elif len(model_data.loaded_sd_models) > 0:
sd_model = model_data.loaded_sd_models.pop()
model_data.sd_model = sd_model
sd_vae.base_vae = getattr(sd_model, "base_vae", None)
sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None)
sd_vae.checkpoint_info = sd_model.sd_checkpoint_info
print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
return sd_model
else:
return None
def reload_model_weights(sd_model=None, info=None, forced_reload=False):
checkpoint_info = info or select_checkpoint()
timer = Timer()
if not sd_model:
sd_model = model_data.sd_model
if sd_model is None: # previous model load failed
current_checkpoint_info = None
else:
current_checkpoint_info = sd_model.sd_checkpoint_info
if check_fp8(sd_model) != devices.fp8:
# load from state dict again to prevent extra numerical errors
forced_reload = True
elif sd_model.sd_model_checkpoint == checkpoint_info.filename and not forced_reload:
return sd_model
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
if not forced_reload and sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
return sd_model
if sd_model is not None:
sd_unet.apply_unet("None")
send_model_to_cpu(sd_model)
sd_hijack.model_hijack.undo_hijack(sd_model)
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
timer.record("find config")
if sd_model is None or checkpoint_config != sd_model.used_config:
if sd_model is not None:
send_model_to_trash(sd_model)
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return model_data.sd_model
try:
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
except Exception:
print("Failed to load checkpoint, restoring previous")
load_model_weights(sd_model, current_checkpoint_info, None, timer)
raise
finally:
sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
if not sd_model.lowvram:
sd_model.to(devices.device)
timer.record("move model to device")
script_callbacks.model_loaded_callback(sd_model)
timer.record("script callbacks")
print(f"Weights loaded in {timer.summary()}.")
model_data.set_sd_model(sd_model)
sd_unet.apply_unet()
return sd_model
def unload_model_weights(sd_model=None, info=None):
send_model_to_cpu(sd_model or shared.sd_model)
return sd_model
def apply_token_merging(sd_model, token_merging_ratio):
"""
Applies speed and memory optimizations from tomesd.
"""
current_token_merging_ratio = getattr(sd_model, 'applied_token_merged_ratio', 0)
if current_token_merging_ratio == token_merging_ratio:
return
if current_token_merging_ratio > 0:
tomesd.remove_patch(sd_model)
if token_merging_ratio > 0:
tomesd.apply_patch(
sd_model,
ratio=token_merging_ratio,
use_rand=False, # can cause issues with some samplers
merge_attn=True,
merge_crossattn=False,
merge_mlp=False
)
sd_model.applied_token_merged_ratio = token_merging_ratio

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#!/bin/bash
####################################################################
# macOS defaults #
# Please modify webui-user.sh to change these instead of this file #
####################################################################
if [[ -x "$(command -v python3.10)" ]]
then
python_cmd="python3.10"
fi
export install_dir="$HOME"
export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
# export COMMANDLINE_ARGS="--skip-torch-cuda-test --opt-sdp-attention --precision full --no-half"
export TORCH_COMMAND="pip install torch==2.1.0 torchvision==0.16.0"
export PYTORCH_ENABLE_MPS_FALLBACK=1
####################################################################

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#!/bin/bash
#########################################################
# Uncomment and change the variables below to your need:#
#########################################################
# Install directory without trailing slash
#install_dir="/home/$(whoami)"
# Name of the subdirectory
#clone_dir="stable-diffusion-webui"
# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
# export COMMANDLINE_ARGS=""
# export COMMANDLINE_ARGS="--skip-torch-cuda-test --opt-sdp-attention --precision full --no-half"
# export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
export COMMANDLINE_ARGS="--skip-torch-cuda-test --opt-sdp-attention --precision full --no-half --upcast-sampling --no-half-vae --use-cpu interrogate"
# python3 executable
#python_cmd="python3"
# git executable
#export GIT="git"
# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
#venv_dir="venv"
# script to launch to start the app
#export LAUNCH_SCRIPT="launch.py"
# install command for torch
#export TORCH_COMMAND="pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113"
# Requirements file to use for stable-diffusion-webui
#export REQS_FILE="requirements_versions.txt"
# Fixed git repos
#export K_DIFFUSION_PACKAGE=""
#export GFPGAN_PACKAGE=""
# Fixed git commits
#export STABLE_DIFFUSION_COMMIT_HASH=""
#export CODEFORMER_COMMIT_HASH=""
#export BLIP_COMMIT_HASH=""
# Uncomment to enable accelerated launch
#export ACCELERATE="True"
# Uncomment to disable TCMalloc
#export NO_TCMALLOC="True"
###########################################

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from __future__ import annotations
import os
import time
from modules import timer
from modules import initialize_util
from modules import initialize
startup_timer = timer.startup_timer
startup_timer.record("launcher")
initialize.imports()
initialize.check_versions()
def create_api(app):
from modules.api.api import Api
from modules.call_queue import queue_lock
api = Api(app, queue_lock)
return api
def api_only():
from fastapi import FastAPI
from modules.shared_cmd_options import cmd_opts
initialize.initialize()
app = FastAPI()
initialize_util.setup_middleware(app)
api = create_api(app)
from modules import script_callbacks
script_callbacks.before_ui_callback()
script_callbacks.app_started_callback(None, app)
print(f"Startup time: {startup_timer.summary()}.")
api.launch(
server_name=initialize_util.gradio_server_name(),
port=cmd_opts.port if cmd_opts.port else 7861,
root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else ""
)
def webui():
from modules.shared_cmd_options import cmd_opts
launch_api = cmd_opts.api
initialize.initialize()
from modules import shared, ui_tempdir, script_callbacks, ui, progress, ui_extra_networks
while 1:
if shared.opts.clean_temp_dir_at_start:
ui_tempdir.cleanup_tmpdr()
startup_timer.record("cleanup temp dir")
script_callbacks.before_ui_callback()
startup_timer.record("scripts before_ui_callback")
shared.demo = ui.create_ui()
startup_timer.record("create ui")
if not cmd_opts.no_gradio_queue:
shared.demo.queue(64)
gradio_auth_creds = list(initialize_util.get_gradio_auth_creds()) or None
auto_launch_browser = False
if os.getenv('SD_WEBUI_RESTARTING') != '1':
if shared.opts.auto_launch_browser == "Remote" or cmd_opts.autolaunch:
auto_launch_browser = True
elif shared.opts.auto_launch_browser == "Local":
auto_launch_browser = not cmd_opts.webui_is_non_local
print("-"*60)
print(f"cmd_opts.share: {cmd_opts.share}")
print(f"set share=True")
app, local_url, share_url = shared.demo.launch(
# share=cmd_opts.share,
share=True,
server_name=initialize_util.gradio_server_name(),
server_port=cmd_opts.port,
ssl_keyfile=cmd_opts.tls_keyfile,
ssl_certfile=cmd_opts.tls_certfile,
ssl_verify=cmd_opts.disable_tls_verify,
debug=cmd_opts.gradio_debug,
auth=gradio_auth_creds,
inbrowser=auto_launch_browser,
prevent_thread_lock=True,
allowed_paths=cmd_opts.gradio_allowed_path,
app_kwargs={
"docs_url": "/docs",
"redoc_url": "/redoc",
},
root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else "",
)
startup_timer.record("gradio launch")
# gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
# an attacker to trick the user into opening a malicious HTML page, which makes a request to the
# running web ui and do whatever the attacker wants, including installing an extension and
# running its code. We disable this here. Suggested by RyotaK.
app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
initialize_util.setup_middleware(app)
progress.setup_progress_api(app)
ui.setup_ui_api(app)
if launch_api:
create_api(app)
ui_extra_networks.add_pages_to_demo(app)
startup_timer.record("add APIs")
with startup_timer.subcategory("app_started_callback"):
script_callbacks.app_started_callback(shared.demo, app)
timer.startup_record = startup_timer.dump()
print(f"Startup time: {startup_timer.summary()}.")
try:
while True:
server_command = shared.state.wait_for_server_command(timeout=5)
if server_command:
if server_command in ("stop", "restart"):
break
else:
print(f"Unknown server command: {server_command}")
except KeyboardInterrupt:
print('Caught KeyboardInterrupt, stopping...')
server_command = "stop"
if server_command == "stop":
print("Stopping server...")
# If we catch a keyboard interrupt, we want to stop the server and exit.
shared.demo.close()
break
# disable auto launch webui in browser for subsequent UI Reload
os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
print('Restarting UI...')
shared.demo.close()
time.sleep(0.5)
startup_timer.reset()
script_callbacks.app_reload_callback()
startup_timer.record("app reload callback")
script_callbacks.script_unloaded_callback()
startup_timer.record("scripts unloaded callback")
initialize.initialize_rest(reload_script_modules=True)
if __name__ == "__main__":
from modules.shared_cmd_options import cmd_opts
if cmd_opts.nowebui:
api_only()
else:
webui()

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#!/usr/bin/env bash
#################################################
# Please do not make any changes to this file, #
# change the variables in webui-user.sh instead #
#################################################
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
# If run from macOS, load defaults from webui-macos-env.sh
if [[ "$OSTYPE" == "darwin"* ]]; then
if [[ -f "$SCRIPT_DIR"/webui-macos-env.sh ]]
then
source "$SCRIPT_DIR"/webui-macos-env.sh
fi
fi
# Read variables from webui-user.sh
# shellcheck source=/dev/null
if [[ -f "$SCRIPT_DIR"/webui-user.sh ]]
then
source "$SCRIPT_DIR"/webui-user.sh
fi
# If $venv_dir is "-", then disable venv support
use_venv=1
if [[ $venv_dir == "-" ]]; then
use_venv=0
fi
# Set defaults
# Install directory without trailing slash
if [[ -z "${install_dir}" ]]
then
install_dir="$SCRIPT_DIR"
fi
# Name of the subdirectory (defaults to stable-diffusion-webui)
if [[ -z "${clone_dir}" ]]
then
clone_dir="stable-diffusion-webui"
fi
# python3 executable
if [[ -z "${python_cmd}" ]]
then
python_cmd="python3"
fi
# git executable
if [[ -z "${GIT}" ]]
then
export GIT="git"
else
export GIT_PYTHON_GIT_EXECUTABLE="${GIT}"
fi
# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
if [[ -z "${venv_dir}" ]] && [[ $use_venv -eq 1 ]]
then
venv_dir="venv"
fi
if [[ -z "${LAUNCH_SCRIPT}" ]]
then
LAUNCH_SCRIPT="launch.py"
fi
# this script cannot be run as root by default
can_run_as_root=0
# read any command line flags to the webui.sh script
while getopts "f" flag > /dev/null 2>&1
do
case ${flag} in
f) can_run_as_root=1;;
*) break;;
esac
done
# Disable sentry logging
export ERROR_REPORTING=FALSE
# Do not reinstall existing pip packages on Debian/Ubuntu
export PIP_IGNORE_INSTALLED=0
# Pretty print
delimiter="################################################################"
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n"
printf "\e[1m\e[34mTested on Debian 11 (Bullseye), Fedora 34+ and openSUSE Leap 15.4 or newer.\e[0m"
printf "\n%s\n" "${delimiter}"
# Do not run as root
if [[ $(id -u) -eq 0 && can_run_as_root -eq 0 ]]
then
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m"
printf "\n%s\n" "${delimiter}"
exit 1
else
printf "\n%s\n" "${delimiter}"
printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)"
printf "\n%s\n" "${delimiter}"
fi
if [[ $(getconf LONG_BIT) = 32 ]]
then
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[31mERROR: Unsupported Running on a 32bit OS\e[0m"
printf "\n%s\n" "${delimiter}"
exit 1
fi
if [[ -d .git ]]
then
printf "\n%s\n" "${delimiter}"
printf "Repo already cloned, using it as install directory"
printf "\n%s\n" "${delimiter}"
install_dir="${PWD}/../"
clone_dir="${PWD##*/}"
fi
# Check prerequisites
gpu_info=$(lspci 2>/dev/null | grep -E "VGA|Display")
case "$gpu_info" in
*"Navi 1"*)
export HSA_OVERRIDE_GFX_VERSION=10.3.0
if [[ -z "${TORCH_COMMAND}" ]]
then
pyv="$(${python_cmd} -c 'import sys; print(".".join(map(str, sys.version_info[0:2])))')"
if [[ $(bc <<< "$pyv <= 3.10") -eq 1 ]]
then
# Navi users will still use torch 1.13 because 2.0 does not seem to work.
export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.6"
else
printf "\e[1m\e[31mERROR: RX 5000 series GPUs must be using at max python 3.10, aborting...\e[0m"
exit 1
fi
fi
;;
*"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
;;
*"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \
export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.7"
;;
*"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
printf "\n%s\n" "${delimiter}"
printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
printf "\n%s\n" "${delimiter}"
;;
*)
;;
esac
if ! echo "$gpu_info" | grep -q "NVIDIA";
then
if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
then
export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2"
elif echo "$gpu_info" | grep -q "Huawei" && [[ -z "${TORCH_COMMAND}" ]]
then
export TORCH_COMMAND="pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu; pip install torch_npu"
fi
fi
for preq in "${GIT}" "${python_cmd}"
do
if ! hash "${preq}" &>/dev/null
then
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}"
printf "\n%s\n" "${delimiter}"
exit 1
fi
done
if [[ $use_venv -eq 1 ]] && ! "${python_cmd}" -c "import venv" &>/dev/null
then
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
printf "\n%s\n" "${delimiter}"
exit 1
fi
cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; }
if [[ -d "${clone_dir}" ]]
then
cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
else
printf "\n%s\n" "${delimiter}"
printf "Clone stable-diffusion-webui"
printf "\n%s\n" "${delimiter}"
"${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}"
cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
fi
if [[ $use_venv -eq 1 ]] && [[ -z "${VIRTUAL_ENV}" ]];
then
printf "\n%s\n" "${delimiter}"
printf "Create and activate python venv"
printf "\n%s\n" "${delimiter}"
cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
if [[ ! -d "${venv_dir}" ]]
then
"${python_cmd}" -m venv "${venv_dir}"
first_launch=1
fi
# shellcheck source=/dev/null
if [[ -f "${venv_dir}"/bin/activate ]]
then
source "${venv_dir}"/bin/activate
else
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
printf "\n%s\n" "${delimiter}"
exit 1
fi
else
printf "\n%s\n" "${delimiter}"
printf "python venv already activate or run without venv: ${VIRTUAL_ENV}"
printf "\n%s\n" "${delimiter}"
fi
# Try using TCMalloc on Linux
prepare_tcmalloc() {
if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then
# check glibc version
LIBC_VER=$(echo $(ldd --version | awk 'NR==1 {print $NF}') | grep -oP '\d+\.\d+')
echo "glibc version is $LIBC_VER"
libc_vernum=$(expr $LIBC_VER)
# Since 2.34 libpthread is integrated into libc.so
libc_v234=2.34
# Define Tcmalloc Libs arrays
TCMALLOC_LIBS=("libtcmalloc(_minimal|)\.so\.\d" "libtcmalloc\.so\.\d")
# Traversal array
for lib in "${TCMALLOC_LIBS[@]}"
do
# Determine which type of tcmalloc library the library supports
TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -P $lib | head -n 1)"
TC_INFO=(${TCMALLOC//=>/})
if [[ ! -z "${TC_INFO}" ]]; then
echo "Check TCMalloc: ${TC_INFO}"
# Determine if the library is linked to libpthread and resolve undefined symbol: pthread_key_create
if [ $(echo "$libc_vernum < $libc_v234" | bc) -eq 1 ]; then
# glibc < 2.34 pthread_key_create into libpthread.so. check linking libpthread.so...
if ldd ${TC_INFO[2]} | grep -q 'libpthread'; then
echo "$TC_INFO is linked with libpthread,execute LD_PRELOAD=${TC_INFO[2]}"
# set fullpath LD_PRELOAD (To be on the safe side)
export LD_PRELOAD="${TC_INFO[2]}"
break
else
echo "$TC_INFO is not linked with libpthread will trigger undefined symbol: pthread_Key_create error"
fi
else
# Version 2.34 of libc.so (glibc) includes the pthread library IN GLIBC. (USE ubuntu 22.04 and modern linux system and WSL)
# libc.so(glibc) is linked with a library that works in ALMOST ALL Linux userlands. SO NO CHECK!
echo "$TC_INFO is linked with libc.so,execute LD_PRELOAD=${TC_INFO[2]}"
# set fullpath LD_PRELOAD (To be on the safe side)
export LD_PRELOAD="${TC_INFO[2]}"
break
fi
fi
done
if [[ -z "${LD_PRELOAD}" ]]; then
printf "\e[1m\e[31mCannot locate TCMalloc. Do you have tcmalloc or google-perftool installed on your system? (improves CPU memory usage)\e[0m\n"
fi
fi
}
KEEP_GOING=1
export SD_WEBUI_RESTART=tmp/restart
while [[ "$KEEP_GOING" -eq "1" ]]; do
if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]; then
printf "\n%s\n" "${delimiter}"
printf "Accelerating launch.py..."
printf "\n%s\n" "${delimiter}"
prepare_tcmalloc
accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
else
printf "\n%s\n" "${delimiter}"
printf "Launching launch.py..."
printf "\n%s\n" "${delimiter}"
prepare_tcmalloc
"${python_cmd}" -u "${LAUNCH_SCRIPT}" "$@"
fi
if [[ ! -f tmp/restart ]]; then
KEEP_GOING=0
fi
done