mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
this is release candidate 2.3.3-rc1 (#3033)
This includes a number of bug fixes described in the draft release notes. It also incorporates a modified version of the dialog-based invoke.sh script suggested by JoshuaKimsey: https://discord.com/channels/1020123559063990373/1089119602425995304
This commit is contained in:
Lincoln Stein
GitHub
commit
77a63e5310
@ -211,6 +211,26 @@ description for the model, whether to make this the default model that
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is loaded at InvokeAI startup time, and whether to replace its
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VAE. Generally the answer to the latter question is "no".
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### Specifying a configuration file for legacy checkpoints
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Some checkpoint files come with instructions to use a specific .yaml
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configuration file. For InvokeAI load this file correctly, please put
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the config file in the same directory as the corresponding `.ckpt` or
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`.safetensors` file and make sure the file has the same basename as
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the weights file. Here is an example:
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```bash
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wonderful-model-v2.ckpt
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wonderful-model-v2.yaml
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```
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Similarly, to use a custom VAE, name the VAE like this:
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```bash
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wonderful-model-v2.vae.pt
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```
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### Converting legacy models into `diffusers`
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The CLI `!convert_model` will convert a `.safetensors` or `.ckpt`
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@ -1,5 +1,8 @@
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#!/bin/bash
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# coauthored by Lincoln Stein, Eugene Brodsky and JoshuaKimsey
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# Copyright 2023, The InvokeAI Development Team
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####
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# This launch script assumes that:
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# 1. it is located in the runtime directory,
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@ -18,78 +21,135 @@ cd "$scriptdir"
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. .venv/bin/activate
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export INVOKEAI_ROOT="$scriptdir"
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PARAMS=$@
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# set required env var for torch on mac MPS
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if [ "$(uname -s)" == "Darwin" ]; then
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export PYTORCH_ENABLE_MPS_FALLBACK=1
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fi
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if [ "$0" != "bash" ]; then
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while true
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do
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echo "Do you want to generate images using the"
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echo "1. command-line interface"
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echo "2. browser-based UI"
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echo "3. run textual inversion training"
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echo "4. merge models (diffusers type only)"
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echo "5. download and install models"
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echo "6. change InvokeAI startup options"
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echo "7. re-run the configure script to fix a broken install"
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echo "8. open the developer console"
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echo "9. update InvokeAI"
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echo "10. command-line help"
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echo "Q - Quit"
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echo ""
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read -p "Please enter 1-10, Q: [2] " yn
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choice=${yn:='2'}
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case $choice in
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1)
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echo "Starting the InvokeAI command-line..."
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invokeai $@
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do_choice() {
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case $1 in
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1)
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echo "Generate images with a browser-based interface"
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clear
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invokeai --web $PARAMS
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;;
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2)
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echo "Starting the InvokeAI browser-based UI..."
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invokeai --web $@
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2)
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echo "Generate images using a command-line interface"
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clear
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invokeai $PARAMS
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;;
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3)
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echo "Starting Textual Inversion:"
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invokeai-ti --gui $@
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3)
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echo "Textual inversion training"
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clear
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invokeai-ti --gui $PARAMS
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;;
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4)
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echo "Merging Models:"
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invokeai-merge --gui $@
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4)
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echo "Merge models (diffusers type only)"
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clear
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invokeai-merge --gui $PARAMS
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;;
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5)
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5)
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echo "Download and install models"
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clear
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invokeai-model-install --root ${INVOKEAI_ROOT}
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;;
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6)
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6)
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echo "Change InvokeAI startup options"
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clear
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invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
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;;
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7)
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7)
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echo "Re-run the configure script to fix a broken install"
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clear
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invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only
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;;
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8)
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echo "Developer Console:"
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8)
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echo "Open the developer console"
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clear
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file_name=$(basename "${BASH_SOURCE[0]}")
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bash --init-file "$file_name"
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;;
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9)
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echo "Update:"
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9)
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echo "Update InvokeAI"
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clear
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invokeai-update
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;;
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10)
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10)
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echo "Command-line help"
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clear
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invokeai --help
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;;
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[qQ])
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exit 0
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*)
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echo "Exiting..."
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exit
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;;
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*)
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echo "Invalid selection"
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exit;;
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esac
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done
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clear
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}
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do_dialog() {
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while true
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do
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options=(
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1 "Generate images with a browser-based interface"
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2 "Generate images using a command-line interface"
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3 "Textual inversion training"
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4 "Merge models (diffusers type only)"
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5 "Download and install models"
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6 "Change InvokeAI startup options"
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7 "Re-run the configure script to fix a broken install"
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8 "Open the developer console"
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9 "Update InvokeAI"
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10 "Command-line help")
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choice=$(dialog --clear \
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--backtitle "InvokeAI" \
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--title "What would you like to run?" \
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--menu "Select an option:" \
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0 0 0 \
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"${options[@]}" \
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2>&1 >/dev/tty) || clear
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do_choice "$choice"
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done
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clear
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}
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do_line_input() {
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echo " ** For a more attractive experience, please install the 'dialog' utility. **"
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echo ""
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while true
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do
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echo "Do you want to generate images using the"
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echo "1. browser-based UI"
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echo "2. command-line interface"
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echo "3. run textual inversion training"
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echo "4. merge models (diffusers type only)"
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echo "5. download and install models"
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echo "6. change InvokeAI startup options"
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echo "7. re-run the configure script to fix a broken install"
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echo "8. open the developer console"
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echo "9. update InvokeAI"
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echo "10. command-line help"
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echo "Q - Quit"
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echo ""
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read -p "Please enter 1-10, Q: [1] " yn
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choice=${yn:='1'}
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do_choice $choice
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done
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}
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if [ "$0" != "bash" ]; then
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# Dialog seems to be a standard installtion for most Linux distros, but this checks to ensure it is present regardless
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if command -v dialog &> /dev/null ; then
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do_dialog
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else
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do_line_input
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fi
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else # in developer console
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python --version
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echo "Press ^D to exit"
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export PS1="(InvokeAI) \u@\h \w> "
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fi
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File diff suppressed because it is too large
Load Diff
@ -19,7 +19,7 @@ import warnings
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from enum import Enum
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from pathlib import Path
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from shutil import move, rmtree
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from typing import Any, Optional, Union, Callable
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from typing import Any, Callable, Optional, Union
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import safetensors
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import safetensors.torch
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@ -35,12 +35,7 @@ from picklescan.scanner import scan_file_path
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from ldm.invoke.devices import CPU_DEVICE
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from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
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from ldm.invoke.globals import Globals, global_cache_dir
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from ldm.util import (
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ask_user,
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download_with_resume,
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instantiate_from_config,
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url_attachment_name,
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)
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from ldm.util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name
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class SDLegacyType(Enum):
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@ -384,15 +379,16 @@ class ModelManager(object):
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if not os.path.isabs(weights):
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weights = os.path.normpath(os.path.join(Globals.root, weights))
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# check whether this is a v2 file and force conversion
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convert = Globals.ckpt_convert or self.is_v2_config(config)
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# if converting automatically to diffusers, then we do the conversion and return
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# a diffusers pipeline
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if Globals.ckpt_convert:
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if convert:
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print(
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f">> Converting legacy checkpoint {model_name} into a diffusers model..."
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)
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from ldm.invoke.ckpt_to_diffuser import (
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load_pipeline_from_original_stable_diffusion_ckpt,
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)
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from ldm.invoke.ckpt_to_diffuser import load_pipeline_from_original_stable_diffusion_ckpt
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self.offload_model(self.current_model)
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if vae_config := self._choose_diffusers_vae(model_name):
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@ -433,13 +429,13 @@ class ModelManager(object):
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weight_bytes = f.read()
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model_hash = self._cached_sha256(weights, weight_bytes)
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sd = None
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if weights.endswith(".ckpt"):
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self.scan_model(model_name, weights)
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sd = torch.load(io.BytesIO(weight_bytes), map_location="cpu")
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else:
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sd = safetensors.torch.load(weight_bytes)
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del weight_bytes
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# merged models from auto11 merge board are flat for some reason
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if "state_dict" in sd:
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@ -462,8 +458,8 @@ class ModelManager(object):
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vae = os.path.normpath(os.path.join(Globals.root, vae))
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if os.path.exists(vae):
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print(f" | Loading VAE weights from: {vae}")
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if vae.endswith((".ckpt",".pt")):
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self.scan_model(vae,vae)
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if vae.endswith((".ckpt", ".pt")):
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self.scan_model(vae, vae)
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vae_ckpt = torch.load(vae, map_location="cpu")
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else:
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vae_ckpt = safetensors.torch.load_file(vae)
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@ -547,6 +543,15 @@ class ModelManager(object):
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return pipeline, width, height, model_hash
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def is_v2_config(self, config: Path) -> bool:
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try:
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mconfig = OmegaConf.load(config)
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return (
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mconfig["model"]["params"]["unet_config"]["params"]["context_dim"] > 768
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)
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except:
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return False
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def model_name_or_path(self, model_name: Union[str, DictConfig]) -> str | Path:
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if isinstance(model_name, DictConfig) or isinstance(model_name, dict):
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mconfig = model_name
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@ -724,7 +729,7 @@ class ModelManager(object):
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SDLegacyType.V2_v (V2 using 'v_prediction' prediction type)
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SDLegacyType.UNKNOWN
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"""
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global_step = checkpoint.get('global_step')
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global_step = checkpoint.get("global_step")
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state_dict = checkpoint.get("state_dict") or checkpoint
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try:
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@ -751,14 +756,14 @@ class ModelManager(object):
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return SDLegacyType.UNKNOWN
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def heuristic_import(
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self,
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path_url_or_repo: str,
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convert: bool = False,
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model_name: str = None,
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description: str = None,
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model_config_file: Path = None,
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commit_to_conf: Path = None,
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config_file_callback: Callable[[Path],Path] = None,
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self,
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path_url_or_repo: str,
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convert: bool = False,
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model_name: str = None,
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description: str = None,
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model_config_file: Path = None,
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commit_to_conf: Path = None,
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config_file_callback: Callable[[Path], Path] = None,
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) -> str:
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"""
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Accept a string which could be:
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@ -833,10 +838,10 @@ class ModelManager(object):
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Path(thing).rglob("*.safetensors")
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):
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if model_name := self.heuristic_import(
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str(m),
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convert,
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commit_to_conf=commit_to_conf,
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config_file_callback=config_file_callback,
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str(m),
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convert,
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commit_to_conf=commit_to_conf,
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config_file_callback=config_file_callback,
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):
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print(f" >> {model_name} successfully imported")
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return model_name
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@ -864,57 +869,66 @@ class ModelManager(object):
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# another round of heuristics to guess the correct config file.
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checkpoint = None
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if model_path.suffix.endswith((".ckpt",".pt")):
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self.scan_model(model_path,model_path)
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if model_path.suffix.endswith((".ckpt", ".pt")):
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self.scan_model(model_path, model_path)
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checkpoint = torch.load(model_path)
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else:
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checkpoint = safetensors.torch.load_file(model_path)
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# additional probing needed if no config file provided
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if model_config_file is None:
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model_type = self.probe_model_type(checkpoint)
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if model_type == SDLegacyType.V1:
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print(" | SD-v1 model detected")
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model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v1-inference.yaml"
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)
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elif model_type == SDLegacyType.V1_INPAINT:
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print(" | SD-v1 inpainting model detected")
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model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v1-inpainting-inference.yaml"
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)
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elif model_type == SDLegacyType.V2_v:
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print(
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" | SD-v2-v model detected"
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)
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model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
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)
|
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elif model_type == SDLegacyType.V2_e:
|
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print(
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" | SD-v2-e model detected"
|
||||
)
|
||||
model_config_file = Path(
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Globals.root, "configs/stable-diffusion/v2-inference.yaml"
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)
|
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elif model_type == SDLegacyType.V2:
|
||||
print(
|
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f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
|
||||
)
|
||||
# Is there a like-named .yaml file in the same directory as the
|
||||
# weights file? If so, we treat this as our model
|
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if model_path.with_suffix(".yaml").exists():
|
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model_config_file = model_path.with_suffix(".yaml")
|
||||
print(f" | Using config file {model_config_file.name}")
|
||||
else:
|
||||
print(
|
||||
f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
|
||||
)
|
||||
model_type = self.probe_model_type(checkpoint)
|
||||
if model_type == SDLegacyType.V1:
|
||||
print(" | SD-v1 model detected")
|
||||
model_config_file = Path(
|
||||
Globals.root, "configs/stable-diffusion/v1-inference.yaml"
|
||||
)
|
||||
elif model_type == SDLegacyType.V1_INPAINT:
|
||||
print(" | SD-v1 inpainting model detected")
|
||||
model_config_file = Path(
|
||||
Globals.root,
|
||||
"configs/stable-diffusion/v1-inpainting-inference.yaml",
|
||||
)
|
||||
elif model_type == SDLegacyType.V2_v:
|
||||
print(" | SD-v2-v model detected")
|
||||
model_config_file = Path(
|
||||
Globals.root, "configs/stable-diffusion/v2-inference-v.yaml"
|
||||
)
|
||||
elif model_type == SDLegacyType.V2_e:
|
||||
print(" | SD-v2-e model detected")
|
||||
model_config_file = Path(
|
||||
Globals.root, "configs/stable-diffusion/v2-inference.yaml"
|
||||
)
|
||||
elif model_type == SDLegacyType.V2:
|
||||
print(
|
||||
f"** {thing} is a V2 checkpoint file, but its parameterization cannot be determined. Please provide configuration file path."
|
||||
)
|
||||
else:
|
||||
print(
|
||||
f"** {thing} is a legacy checkpoint file but not a known Stable Diffusion model. Please provide configuration file path."
|
||||
)
|
||||
|
||||
if not model_config_file and config_file_callback:
|
||||
model_config_file = config_file_callback(model_path)
|
||||
if not model_config_file:
|
||||
return
|
||||
|
||||
if model_config_file.name.startswith('v2'):
|
||||
if self.is_v2_config(model_config_file):
|
||||
convert = True
|
||||
print(
|
||||
" | This SD-v2 model will be converted to diffusers format for use"
|
||||
)
|
||||
print(" | This SD-v2 model will be converted to diffusers format for use")
|
||||
|
||||
# look for a custom vae
|
||||
vae_path = None
|
||||
for suffix in ["pt", "ckpt", "safetensors"]:
|
||||
if (model_path.with_suffix(f".vae.{suffix}")).exists():
|
||||
vae_path = model_path.with_suffix(f".vae.{suffix}")
|
||||
print(f" | Using VAE file {vae_path.name}")
|
||||
vae = None if vae_path else dict(repo_id="stabilityai/sd-vae-ft-mse")
|
||||
|
||||
if convert:
|
||||
diffuser_path = Path(
|
||||
@ -923,7 +937,8 @@ class ModelManager(object):
|
||||
model_name = self.convert_and_import(
|
||||
model_path,
|
||||
diffusers_path=diffuser_path,
|
||||
vae=dict(repo_id="stabilityai/sd-vae-ft-mse"),
|
||||
vae=vae,
|
||||
vae_path=vae_path,
|
||||
model_name=model_name,
|
||||
model_description=description,
|
||||
original_config_file=model_config_file,
|
||||
@ -941,7 +956,8 @@ class ModelManager(object):
|
||||
model_name=model_name,
|
||||
model_description=description,
|
||||
vae=str(
|
||||
Path(
|
||||
vae_path
|
||||
or Path(
|
||||
Globals.root,
|
||||
"models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt",
|
||||
)
|
||||
@ -953,15 +969,16 @@ class ModelManager(object):
|
||||
return model_name
|
||||
|
||||
def convert_and_import(
|
||||
self,
|
||||
ckpt_path: Path,
|
||||
diffusers_path: Path,
|
||||
model_name=None,
|
||||
model_description=None,
|
||||
vae=None,
|
||||
original_config_file: Path = None,
|
||||
commit_to_conf: Path = None,
|
||||
scan_needed: bool=True,
|
||||
self,
|
||||
ckpt_path: Path,
|
||||
diffusers_path: Path,
|
||||
model_name=None,
|
||||
model_description=None,
|
||||
vae: dict = None,
|
||||
vae_path: Path = None,
|
||||
original_config_file: Path = None,
|
||||
commit_to_conf: Path = None,
|
||||
scan_needed: bool = True,
|
||||
) -> str:
|
||||
"""
|
||||
Convert a legacy ckpt weights file to diffuser model and import
|
||||
@ -975,7 +992,7 @@ class ModelManager(object):
|
||||
|
||||
new_config = None
|
||||
|
||||
from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
|
||||
from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffusers
|
||||
|
||||
if diffusers_path.exists():
|
||||
print(
|
||||
@ -990,12 +1007,13 @@ class ModelManager(object):
|
||||
# By passing the specified VAE to the conversion function, the autoencoder
|
||||
# will be built into the model rather than tacked on afterward via the config file
|
||||
vae_model = self._load_vae(vae) if vae else None
|
||||
convert_ckpt_to_diffuser(
|
||||
convert_ckpt_to_diffusers(
|
||||
ckpt_path,
|
||||
diffusers_path,
|
||||
extract_ema=True,
|
||||
original_config_file=original_config_file,
|
||||
vae=vae_model,
|
||||
vae_path=str(vae_path) if vae_path else None,
|
||||
scan_needed=scan_needed,
|
||||
)
|
||||
print(
|
||||
@ -1048,7 +1066,7 @@ class ModelManager(object):
|
||||
# In the event that the original entry is using a custom ckpt VAE, we try to
|
||||
# map that VAE onto a diffuser VAE using a hard-coded dictionary.
|
||||
# I would prefer to do this differently: We load the ckpt model into memory, swap the
|
||||
# VAE in memory, and then pass that to convert_ckpt_to_diffuser() so that the swapped
|
||||
# VAE in memory, and then pass that to convert_ckpt_to_diffusers() so that the swapped
|
||||
# VAE is built into the model. However, when I tried this I got obscure key errors.
|
||||
if vae:
|
||||
return vae
|
||||
@ -1134,14 +1152,14 @@ class ModelManager(object):
|
||||
legacy_locations = [
|
||||
Path(
|
||||
models_dir,
|
||||
"CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker"
|
||||
"CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker",
|
||||
),
|
||||
Path("bert-base-uncased/models--bert-base-uncased"),
|
||||
Path(
|
||||
"openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14"
|
||||
),
|
||||
]
|
||||
legacy_locations.extend(list(global_cache_dir("diffusers").glob('*')))
|
||||
legacy_locations.extend(list(global_cache_dir("diffusers").glob("*")))
|
||||
legacy_layout = False
|
||||
for model in legacy_locations:
|
||||
legacy_layout = legacy_layout or model.exists()
|
||||
@ -1185,7 +1203,7 @@ class ModelManager(object):
|
||||
source.unlink()
|
||||
else:
|
||||
move(source, dest)
|
||||
|
||||
|
||||
# now clean up by removing any empty directories
|
||||
empty = [
|
||||
root
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user