forked from extern/easydiffusion
Merge branch 'beta' into serverip
This commit is contained in:
commit
b96b55c5ce
27
3rd-PARTY-LICENSES
Normal file
27
3rd-PARTY-LICENSES
Normal file
@ -0,0 +1,27 @@
|
||||
jquery-confirm
|
||||
==============
|
||||
https://craftpip.github.io/jquery-confirm/
|
||||
|
||||
jquery-confirm is licensed under the MIT license:
|
||||
|
||||
The MIT License (MIT)
|
||||
|
||||
Copyright (c) 2019 Boniface Pereira
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
@ -19,10 +19,14 @@
|
||||
- Configuration to prevent the browser from opening on startup
|
||||
- Lots of minor bug fixes
|
||||
- A `What's New?` tab in the UI
|
||||
- Button to retrieve the network addresses of the server in the systems setting dialog
|
||||
- Ask for a confimation before clearing the results pane or stopping a render task. The dialog can be skipped by holding down the shift key while clicking on the button.
|
||||
- Show the network addresses of the server in the systems setting dialog
|
||||
|
||||
### Detailed changelog
|
||||
* 2.4.14 - 23 Nov 2022 - Button to retrieve the network addresses of the server in the systems setting dialog
|
||||
* 2.4.17 - 30 Nov 2022 - Show the network addresses of the server in the systems setting dialog
|
||||
* 2.4.17 - 30 Nov 2022 - Confirm before stopping or clearing all the tasks
|
||||
* 2.4.16 - 29 Nov 2022 - Bug fixes for SD 2.0 - remove the need for patching, default to SD 1.4 model if trying to load an SD2 model in SD1.4.
|
||||
* 2.4.15 - 25 Nov 2022 - Experimental support for SD 2.0. Uses lots of memory, not optimized, probably GPU-only.
|
||||
* 2.4.14 - 22 Nov 2022 - Change the backend to a custom fork of Stable Diffusion
|
||||
* 2.4.13 - 21 Nov 2022 - Change the modifier weight via mouse wheel, drag to reorder selected modifiers, and some more modifier-related fixes. Thanks @patriceac
|
||||
* 2.4.12 - 21 Nov 2022 - Another fix for improving how long images take to generate. Reduces the time taken for an enqueued task to start processing.
|
||||
|
@ -29,6 +29,18 @@ call conda activate .\stable-diffusion\env
|
||||
call where python
|
||||
call python --version
|
||||
|
||||
@rem set the PYTHONPATH
|
||||
cd stable-diffusion
|
||||
set SD_DIR=%cd%
|
||||
|
||||
cd env\lib\site-packages
|
||||
set PYTHONPATH=%SD_DIR%;%cd%
|
||||
cd ..\..\..
|
||||
echo PYTHONPATH=%PYTHONPATH%
|
||||
|
||||
cd ..
|
||||
|
||||
@rem done
|
||||
echo.
|
||||
|
||||
cmd /k
|
||||
|
@ -35,6 +35,15 @@ if [ "$0" == "bash" ]; then
|
||||
which python
|
||||
python --version
|
||||
|
||||
# set the PYTHONPATH
|
||||
cd stable-diffusion
|
||||
SD_PATH=`pwd`
|
||||
export PYTHONPATH="$SD_PATH:$SD_PATH/env/lib/python3.8/site-packages"
|
||||
echo "PYTHONPATH=$PYTHONPATH"
|
||||
cd ..
|
||||
|
||||
# done
|
||||
|
||||
echo ""
|
||||
else
|
||||
file_name=$(basename "${BASH_SOURCE[0]}")
|
||||
|
@ -27,6 +27,8 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
|
||||
|
||||
@call python -c "import os; import shutil; frm = 'sd-ui-files\\ui\\hotfix\\9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'; dst = os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'transformers', '9c24e6cd9f499d02c4f21a033736dabd365962dc80fe3aeb57a8f85ea45a20a3.26fead7ea4f0f843f6eb4055dfd25693f1a71f3c6871b184042d4b126244e142'); shutil.copyfile(frm, dst) if os.path.exists(dst) else print(''); print('Hotfixed broken JSON file from OpenAI');"
|
||||
|
||||
if NOT DEFINED test_sd2 set test_sd2=N
|
||||
|
||||
@>nul findstr /m "sd_git_cloned" scripts\install_status.txt
|
||||
@if "%ERRORLEVEL%" EQU "0" (
|
||||
@echo "Stable Diffusion's git repository was already installed. Updating.."
|
||||
@ -37,9 +39,13 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
|
||||
|
||||
@call git reset --hard
|
||||
@call git pull
|
||||
@call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
||||
|
||||
@call git apply --whitespace=warn ..\ui\sd_internal\ddim_callback.patch
|
||||
if "%test_sd2%" == "N" (
|
||||
@call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
||||
)
|
||||
if "%test_sd2%" == "Y" (
|
||||
@call git -c advice.detachedHead=false checkout 8878d67decd3deb3c98472c1e39d2a51dc5950f9
|
||||
)
|
||||
|
||||
@cd ..
|
||||
) else (
|
||||
@ -56,8 +62,6 @@ if exist "Open Developer Console.cmd" del "Open Developer Console.cmd"
|
||||
@cd stable-diffusion
|
||||
@call git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
||||
|
||||
@call git apply --whitespace=warn ..\ui\sd_internal\ddim_callback.patch
|
||||
|
||||
@cd ..
|
||||
)
|
||||
|
||||
@ -346,7 +350,9 @@ echo. > "..\models\vae\Put your VAE files here.txt"
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
if "%test_sd2%" == "Y" (
|
||||
@call pip install open_clip_torch==2.0.2
|
||||
)
|
||||
|
||||
@>nul findstr /m "sd_install_complete" ..\scripts\install_status.txt
|
||||
@if "%ERRORLEVEL%" NEQ "0" (
|
||||
|
16
scripts/on_sd_start.sh
Normal file → Executable file
16
scripts/on_sd_start.sh
Normal file → Executable file
@ -21,6 +21,10 @@ python -c "import os; import shutil; frm = 'sd-ui-files/ui/hotfix/9c24e6cd9f499d
|
||||
# Caution, this file will make your eyes and brain bleed. It's such an unholy mess.
|
||||
# Note to self: Please rewrite this in Python. For the sake of your own sanity.
|
||||
|
||||
if [ "$test_sd2" == "" ]; then
|
||||
export test_sd2="N"
|
||||
fi
|
||||
|
||||
if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/install_status.txt` -gt "0" ]; then
|
||||
echo "Stable Diffusion's git repository was already installed. Updating.."
|
||||
|
||||
@ -30,9 +34,12 @@ if [ -e "scripts/install_status.txt" ] && [ `grep -c sd_git_cloned scripts/insta
|
||||
|
||||
git reset --hard
|
||||
git pull
|
||||
git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
||||
|
||||
git apply --whitespace=warn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
|
||||
if [ "$test_sd2" == "N" ]; then
|
||||
git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
||||
elif [ "$test_sd2" == "Y" ]; then
|
||||
git -c advice.detachedHead=false checkout 8878d67decd3deb3c98472c1e39d2a51dc5950f9
|
||||
fi
|
||||
|
||||
cd ..
|
||||
else
|
||||
@ -47,8 +54,6 @@ else
|
||||
cd stable-diffusion
|
||||
git -c advice.detachedHead=false checkout 7f32368ed1030a6e710537047bacd908adea183a
|
||||
|
||||
git apply --whitespace=warn ../ui/sd_internal/ddim_callback.patch || fail "ddim patch failed"
|
||||
|
||||
cd ..
|
||||
fi
|
||||
|
||||
@ -291,6 +296,9 @@ if [ ! -f "../models/vae/vae-ft-mse-840000-ema-pruned.ckpt" ]; then
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ "$test_sd2" == "Y" ]; then
|
||||
pip install open_clip_torch==2.0.2
|
||||
fi
|
||||
|
||||
if [ `grep -c sd_install_complete ../scripts/install_status.txt` -gt "0" ]; then
|
||||
echo sd_weights_downloaded >> ../scripts/install_status.txt
|
||||
|
@ -12,7 +12,9 @@
|
||||
<link rel="stylesheet" href="/media/css/modifier-thumbnails.css">
|
||||
<link rel="stylesheet" href="/media/css/fontawesome-all.min.css">
|
||||
<link rel="stylesheet" href="/media/css/drawingboard.min.css">
|
||||
<link rel="stylesheet" href="/media/css//jquery-confirm.min.css">
|
||||
<script src="/media/js/jquery-3.6.1.min.js"></script>
|
||||
<script src="/media/js/jquery-confirm.min.js"></script>
|
||||
<script src="/media/js/drawingboard.min.js"></script>
|
||||
<script src="/media/js/marked.min.js"></script>
|
||||
</head>
|
||||
@ -22,7 +24,7 @@
|
||||
<div id="logo">
|
||||
<h1>
|
||||
Stable Diffusion UI
|
||||
<small>v2.4.14 <span id="updateBranchLabel"></span></small>
|
||||
<small>v2.4.16 <span id="updateBranchLabel"></span></small>
|
||||
</h1>
|
||||
</div>
|
||||
<div id="server-status">
|
||||
|
9
ui/media/css/jquery-confirm.min.css
vendored
Normal file
9
ui/media/css/jquery-confirm.min.css
vendored
Normal file
File diff suppressed because one or more lines are too long
@ -210,7 +210,7 @@ code {
|
||||
}
|
||||
.collapsible-content {
|
||||
display: block;
|
||||
padding-left: 15px;
|
||||
padding-left: 10px;
|
||||
}
|
||||
.collapsible-content h5 {
|
||||
padding: 5pt 0pt;
|
||||
@ -658,11 +658,15 @@ input::file-selector-button {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
/* MOBILE SUPPORT */
|
||||
@media screen and (max-width: 700px) {
|
||||
/* Small screens */
|
||||
@media screen and (max-width: 1265px) {
|
||||
#top-nav {
|
||||
flex-direction: column;
|
||||
}
|
||||
}
|
||||
|
||||
/* MOBILE SUPPORT */
|
||||
@media screen and (max-width: 700px) {
|
||||
body {
|
||||
margin: 0px;
|
||||
}
|
||||
@ -712,7 +716,7 @@ input::file-selector-button {
|
||||
padding-right: 0px;
|
||||
}
|
||||
#server-status {
|
||||
display: none;
|
||||
top: 75%;
|
||||
}
|
||||
.popup > div {
|
||||
padding-left: 5px !important;
|
||||
@ -730,6 +734,15 @@ input::file-selector-button {
|
||||
}
|
||||
}
|
||||
|
||||
@media screen and (max-width: 500px) {
|
||||
#server-status #server-status-msg {
|
||||
display: none;
|
||||
}
|
||||
#server-status:hover #server-status-msg {
|
||||
display: inline;
|
||||
}
|
||||
}
|
||||
|
||||
@media (min-width: 700px) {
|
||||
/* #editor {
|
||||
max-width: 480px;
|
||||
|
@ -35,6 +35,7 @@ const SETTINGS_IDS_LIST = [
|
||||
"sound_toggle",
|
||||
"turbo",
|
||||
"use_full_precision",
|
||||
"confirm_dangerous_actions",
|
||||
"auto_save_settings"
|
||||
]
|
||||
|
||||
|
@ -90,9 +90,7 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
|
||||
if (activeTags.map(x => x.name).includes(modifierName)) {
|
||||
// remove modifier from active array
|
||||
activeTags = activeTags.filter(x => x.name != modifierName)
|
||||
modifierCard.classList.remove(activeCardClass)
|
||||
|
||||
modifierCard.querySelector('.modifier-card-image-overlay').innerText = '+'
|
||||
toggleCardState(modifierCard, false)
|
||||
} else {
|
||||
// add modifier to active array
|
||||
activeTags.push({
|
||||
@ -101,10 +99,7 @@ function createModifierGroup(modifierGroup, initiallyExpanded) {
|
||||
'originElement': modifierCard,
|
||||
'previews': modifierPreviews
|
||||
})
|
||||
|
||||
modifierCard.classList.add(activeCardClass)
|
||||
|
||||
modifierCard.querySelector('.modifier-card-image-overlay').innerText = '-'
|
||||
toggleCardState(modifierCard, true)
|
||||
}
|
||||
|
||||
refreshTagsList()
|
||||
@ -222,8 +217,7 @@ function refreshTagsList() {
|
||||
let idx = activeTags.indexOf(tag)
|
||||
|
||||
if (idx !== -1 && activeTags[idx].originElement !== undefined) {
|
||||
activeTags[idx].originElement.classList.remove(activeCardClass)
|
||||
activeTags[idx].originElement.querySelector('.modifier-card-image-overlay').innerText = '+'
|
||||
toggleCardState(activeTags[idx].originElement, false)
|
||||
|
||||
activeTags.splice(idx, 1)
|
||||
refreshTagsList()
|
||||
@ -236,6 +230,16 @@ function refreshTagsList() {
|
||||
editorModifierTagsList.appendChild(brk)
|
||||
}
|
||||
|
||||
function toggleCardState(card, makeActive) {
|
||||
if (makeActive) {
|
||||
card.classList.add(activeCardClass)
|
||||
card.querySelector('.modifier-card-image-overlay').innerText = '-'
|
||||
} else {
|
||||
card.classList.remove(activeCardClass)
|
||||
card.querySelector('.modifier-card-image-overlay').innerText = '+'
|
||||
}
|
||||
}
|
||||
|
||||
function changePreviewImages(val) {
|
||||
const previewImages = document.querySelectorAll('.modifier-card-image-container img')
|
||||
|
||||
|
10
ui/media/js/jquery-confirm.min.js
vendored
Normal file
10
ui/media/js/jquery-confirm.min.js
vendored
Normal file
File diff suppressed because one or more lines are too long
@ -138,6 +138,33 @@ function isServerAvailable() {
|
||||
}
|
||||
}
|
||||
|
||||
// shiftOrConfirm(e, prompt, fn)
|
||||
// e : MouseEvent
|
||||
// prompt : Text to be shown as prompt. Should be a question to which "yes" is a good answer.
|
||||
// fn : function to be called if the user confirms the dialog or has the shift key pressed
|
||||
//
|
||||
// If the user had the shift key pressed while clicking, the function fn will be executed.
|
||||
// If the setting "confirm_dangerous_actions" in the system settings is disabled, the function
|
||||
// fn will be executed.
|
||||
// Otherwise, a confirmation dialog is shown. If the user confirms, the function fn will also
|
||||
// be executed.
|
||||
function shiftOrConfirm(e, prompt, fn) {
|
||||
e.stopPropagation()
|
||||
if (e.shiftKey || !confirmDangerousActionsField.checked) {
|
||||
fn(e)
|
||||
} else {
|
||||
$.confirm({ theme: 'supervan',
|
||||
title: prompt,
|
||||
content: 'Tip: To skip this dialog, use shift-click or disable the setting "Confirm dangerous actions" in the systems setting.',
|
||||
buttons: {
|
||||
yes: () => { fn(e) },
|
||||
cancel: () => {}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
function logMsg(msg, level, outputMsg) {
|
||||
if (outputMsg.hasChildNodes()) {
|
||||
outputMsg.appendChild(document.createElement('br'))
|
||||
@ -887,8 +914,7 @@ function createTask(task) {
|
||||
task['progressBar'] = taskEntry.querySelector('.progress-bar')
|
||||
task['stopTask'] = taskEntry.querySelector('.stopTask')
|
||||
|
||||
task['stopTask'].addEventListener('click', async function(e) {
|
||||
e.stopPropagation()
|
||||
task['stopTask'].addEventListener('click', (e) => { shiftOrConfirm(e, "Are you sure? Should this task be stopped?", async function(e) {
|
||||
if (task['isProcessing']) {
|
||||
task.isProcessing = false
|
||||
task.progressBar.classList.remove("active")
|
||||
@ -905,7 +931,7 @@ function createTask(task) {
|
||||
|
||||
taskEntry.remove()
|
||||
}
|
||||
})
|
||||
})})
|
||||
|
||||
task['useSettings'] = taskEntry.querySelector('.useSettings')
|
||||
task['useSettings'].addEventListener('click', function(e) {
|
||||
@ -934,10 +960,10 @@ function getPrompts() {
|
||||
prompts = prompts.filter(prompt => prompt !== '')
|
||||
|
||||
if (activeTags.length > 0) {
|
||||
const promptTags = activeTags.map(x => x.name).join(", ")
|
||||
prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
|
||||
const promptTags = activeTags.map(x => x.name).join(", ")
|
||||
prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
|
||||
}
|
||||
|
||||
|
||||
let promptsToMake = applySetOperator(prompts)
|
||||
promptsToMake = applyPermuteOperator(promptsToMake)
|
||||
|
||||
@ -1047,7 +1073,7 @@ async function stopAllTasks() {
|
||||
}
|
||||
}
|
||||
|
||||
clearAllPreviewsBtn.addEventListener('click', async function() {
|
||||
clearAllPreviewsBtn.addEventListener('click', (e) => { shiftOrConfirm(e, "Are you sure? Remove all results and tasks from the results pane?", async function() {
|
||||
await stopAllTasks()
|
||||
|
||||
let taskEntries = document.querySelectorAll('.imageTaskContainer')
|
||||
@ -1057,11 +1083,11 @@ clearAllPreviewsBtn.addEventListener('click', async function() {
|
||||
|
||||
previewTools.style.display = 'none'
|
||||
initialText.style.display = 'block'
|
||||
})
|
||||
})})
|
||||
|
||||
stopImageBtn.addEventListener('click', async function() {
|
||||
stopImageBtn.addEventListener('click', (e) => { shiftOrConfirm(e, "Are you sure? Do you want to stop all the tasks?", async function(e) {
|
||||
await stopAllTasks()
|
||||
})
|
||||
})})
|
||||
|
||||
widthField.addEventListener('change', onDimensionChange)
|
||||
heightField.addEventListener('change', onDimensionChange)
|
||||
|
@ -5,9 +5,9 @@
|
||||
*/
|
||||
var ParameterType = {
|
||||
checkbox: "checkbox",
|
||||
select: "select",
|
||||
select_multiple: "select_multiple",
|
||||
custom: "custom",
|
||||
select: "select",
|
||||
select_multiple: "select_multiple",
|
||||
custom: "custom",
|
||||
};
|
||||
|
||||
/**
|
||||
@ -23,166 +23,182 @@
|
||||
|
||||
/** @type {Array.<Parameter>} */
|
||||
var PARAMETERS = [
|
||||
{
|
||||
id: "theme",
|
||||
type: ParameterType.select,
|
||||
label: "Theme",
|
||||
default: "theme-default",
|
||||
note: "customize the look and feel of the ui",
|
||||
options: [ // Note: options expanded dynamically
|
||||
{
|
||||
value: "theme-default",
|
||||
label: "Default"
|
||||
}
|
||||
],
|
||||
icon: "fa-palette"
|
||||
},
|
||||
{
|
||||
id: "save_to_disk",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Auto-Save Images",
|
||||
note: "automatically saves images to the specified location",
|
||||
icon: "fa-download",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "diskPath",
|
||||
type: ParameterType.custom,
|
||||
label: "Save Location",
|
||||
render: (parameter) => {
|
||||
return `<input id="${parameter.id}" name="${parameter.id}" size="30" disabled>`
|
||||
}
|
||||
},
|
||||
{
|
||||
id: "sound_toggle",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Enable Sound",
|
||||
note: "plays a sound on task completion",
|
||||
icon: "fa-volume-low",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "ui_open_browser_on_start",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Open browser on startup",
|
||||
note: "starts the default browser on startup",
|
||||
icon: "fa-window-restore",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "turbo",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Turbo Mode",
|
||||
note: "generates images faster, but uses an additional 1 GB of GPU memory",
|
||||
icon: "fa-forward",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "use_cpu",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Use CPU (not GPU)",
|
||||
note: "warning: this will be *very* slow",
|
||||
icon: "fa-microchip",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "auto_pick_gpus",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Automatically pick the GPUs (experimental)",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "use_gpus",
|
||||
type: ParameterType.select_multiple,
|
||||
label: "GPUs to use (experimental)",
|
||||
note: "to process in parallel",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "use_full_precision",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Use Full Precision",
|
||||
note: "for GPU-only. warning: this will consume more VRAM",
|
||||
icon: "fa-crosshairs",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "auto_save_settings",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Auto-Save Settings",
|
||||
note: "restores settings on browser load",
|
||||
icon: "fa-gear",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "listen_to_network",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Make Stable Diffusion available on your network",
|
||||
note: "Other devices on your network can access this web page",
|
||||
icon: "fa-network-wired",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "listen_port",
|
||||
type: ParameterType.custom,
|
||||
label: "Network port",
|
||||
note: "Port that this server listens to. The '9000' part in 'http://localhost:9000'",
|
||||
icon: "fa-anchor",
|
||||
render: (parameter) => {
|
||||
return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
|
||||
}
|
||||
},
|
||||
{
|
||||
id: "use_beta_channel",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Beta channel",
|
||||
note: "Get the latest features immediately (but could be less stable). Please restart the program after changing this.",
|
||||
icon: "fa-fire",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "theme",
|
||||
type: ParameterType.select,
|
||||
label: "Theme",
|
||||
default: "theme-default",
|
||||
note: "customize the look and feel of the ui",
|
||||
options: [ // Note: options expanded dynamically
|
||||
{
|
||||
value: "theme-default",
|
||||
label: "Default"
|
||||
}
|
||||
],
|
||||
icon: "fa-palette"
|
||||
},
|
||||
{
|
||||
id: "save_to_disk",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Auto-Save Images",
|
||||
note: "automatically saves images to the specified location",
|
||||
icon: "fa-download",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "diskPath",
|
||||
type: ParameterType.custom,
|
||||
label: "Save Location",
|
||||
render: (parameter) => {
|
||||
return `<input id="${parameter.id}" name="${parameter.id}" size="30" disabled>`
|
||||
}
|
||||
},
|
||||
{
|
||||
id: "sound_toggle",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Enable Sound",
|
||||
note: "plays a sound on task completion",
|
||||
icon: "fa-volume-low",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "ui_open_browser_on_start",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Open browser on startup",
|
||||
note: "starts the default browser on startup",
|
||||
icon: "fa-window-restore",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "turbo",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Turbo Mode",
|
||||
note: "generates images faster, but uses an additional 1 GB of GPU memory",
|
||||
icon: "fa-forward",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "use_cpu",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Use CPU (not GPU)",
|
||||
note: "warning: this will be *very* slow",
|
||||
icon: "fa-microchip",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "auto_pick_gpus",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Automatically pick the GPUs (experimental)",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "use_gpus",
|
||||
type: ParameterType.select_multiple,
|
||||
label: "GPUs to use (experimental)",
|
||||
note: "to process in parallel",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "use_full_precision",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Use Full Precision",
|
||||
note: "for GPU-only. warning: this will consume more VRAM",
|
||||
icon: "fa-crosshairs",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "auto_save_settings",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Auto-Save Settings",
|
||||
note: "restores settings on browser load",
|
||||
icon: "fa-gear",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "confirm_dangerous_actions",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Confirm dangerous actions",
|
||||
note: "Actions that might lead to data loss must either be clicked with the shift key pressed, or confirmed in an 'Are you sure?' dialog",
|
||||
icon: "fa-check-double",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "listen_to_network",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Make Stable Diffusion available on your network",
|
||||
note: "Other devices on your network can access this web page",
|
||||
icon: "fa-network-wired",
|
||||
default: true,
|
||||
},
|
||||
{
|
||||
id: "listen_port",
|
||||
type: ParameterType.custom,
|
||||
label: "Network port",
|
||||
note: "Port that this server listens to. The '9000' part in 'http://localhost:9000'",
|
||||
icon: "fa-anchor",
|
||||
render: (parameter) => {
|
||||
return `<input id="${parameter.id}" name="${parameter.id}" size="6" value="9000" onkeypress="preventNonNumericalInput(event)">`
|
||||
}
|
||||
},
|
||||
{
|
||||
id: "test_sd2",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Test SD 2.0",
|
||||
note: "Experimental! High memory usage! GPU-only! Not the final version! Please restart the program after changing this.",
|
||||
icon: "fa-fire",
|
||||
default: false,
|
||||
},
|
||||
{
|
||||
id: "use_beta_channel",
|
||||
type: ParameterType.checkbox,
|
||||
label: "Beta channel",
|
||||
note: "Get the latest features immediately (but could be less stable). Please restart the program after changing this.",
|
||||
icon: "fa-fire",
|
||||
default: false,
|
||||
},
|
||||
];
|
||||
|
||||
function getParameterSettingsEntry(id) {
|
||||
let parameter = PARAMETERS.filter(p => p.id === id)
|
||||
if (parameter.length === 0) {
|
||||
return
|
||||
}
|
||||
return parameter[0].settingsEntry
|
||||
let parameter = PARAMETERS.filter(p => p.id === id)
|
||||
if (parameter.length === 0) {
|
||||
return
|
||||
}
|
||||
return parameter[0].settingsEntry
|
||||
}
|
||||
|
||||
function getParameterElement(parameter) {
|
||||
switch (parameter.type) {
|
||||
case ParameterType.checkbox:
|
||||
var is_checked = parameter.default ? " checked" : "";
|
||||
return `<input id="${parameter.id}" name="${parameter.id}"${is_checked} type="checkbox">`
|
||||
case ParameterType.select:
|
||||
case ParameterType.select_multiple:
|
||||
var options = (parameter.options || []).map(option => `<option value="${option.value}">${option.label}</option>`).join("")
|
||||
var multiple = (parameter.type == ParameterType.select_multiple ? 'multiple' : '')
|
||||
return `<select id="${parameter.id}" name="${parameter.id}" ${multiple}>${options}</select>`
|
||||
case ParameterType.custom:
|
||||
return parameter.render(parameter)
|
||||
default:
|
||||
console.error(`Invalid type for parameter ${parameter.id}`);
|
||||
return "ERROR: Invalid Type"
|
||||
}
|
||||
switch (parameter.type) {
|
||||
case ParameterType.checkbox:
|
||||
var is_checked = parameter.default ? " checked" : "";
|
||||
return `<input id="${parameter.id}" name="${parameter.id}"${is_checked} type="checkbox">`
|
||||
case ParameterType.select:
|
||||
case ParameterType.select_multiple:
|
||||
var options = (parameter.options || []).map(option => `<option value="${option.value}">${option.label}</option>`).join("")
|
||||
var multiple = (parameter.type == ParameterType.select_multiple ? 'multiple' : '')
|
||||
return `<select id="${parameter.id}" name="${parameter.id}" ${multiple}>${options}</select>`
|
||||
case ParameterType.custom:
|
||||
return parameter.render(parameter)
|
||||
default:
|
||||
console.error(`Invalid type for parameter ${parameter.id}`);
|
||||
return "ERROR: Invalid Type"
|
||||
}
|
||||
}
|
||||
|
||||
let parametersTable = document.querySelector("#system-settings .parameters-table")
|
||||
/* fill in the system settings popup table */
|
||||
function initParameters() {
|
||||
PARAMETERS.forEach(parameter => {
|
||||
var element = getParameterElement(parameter)
|
||||
var note = parameter.note ? `<small>${parameter.note}</small>` : "";
|
||||
var icon = parameter.icon ? `<i class="fa ${parameter.icon}"></i>` : "";
|
||||
var newrow = document.createElement('div')
|
||||
newrow.innerHTML = `
|
||||
<div>${icon}</div>
|
||||
<div><label for="${parameter.id}">${parameter.label}</label>${note}</div>
|
||||
<div>${element}</div>`
|
||||
parametersTable.appendChild(newrow)
|
||||
parameter.settingsEntry = newrow
|
||||
})
|
||||
PARAMETERS.forEach(parameter => {
|
||||
var element = getParameterElement(parameter)
|
||||
var note = parameter.note ? `<small>${parameter.note}</small>` : "";
|
||||
var icon = parameter.icon ? `<i class="fa ${parameter.icon}"></i>` : "";
|
||||
var newrow = document.createElement('div')
|
||||
newrow.innerHTML = `
|
||||
<div>${icon}</div>
|
||||
<div><label for="${parameter.id}">${parameter.label}</label>${note}</div>
|
||||
<div>${element}</div>`
|
||||
parametersTable.appendChild(newrow)
|
||||
parameter.settingsEntry = newrow
|
||||
})
|
||||
}
|
||||
|
||||
initParameters()
|
||||
@ -196,8 +212,10 @@ let saveToDiskField = document.querySelector('#save_to_disk')
|
||||
let diskPathField = document.querySelector('#diskPath')
|
||||
let listenToNetworkField = document.querySelector("#listen_to_network")
|
||||
let listenPortField = document.querySelector("#listen_port")
|
||||
let testSD2Field = document.querySelector("#test_sd2")
|
||||
let useBetaChannelField = document.querySelector("#use_beta_channel")
|
||||
let uiOpenBrowserOnStartField = document.querySelector("#ui_open_browser_on_start")
|
||||
let confirmDangerousActionsField = document.querySelector("#confirm_dangerous_actions")
|
||||
|
||||
let saveSettingsBtn = document.querySelector('#save-system-settings-btn')
|
||||
|
||||
@ -234,12 +252,18 @@ async function getAppConfig() {
|
||||
if (config.ui && config.ui.open_browser_on_start === false) {
|
||||
uiOpenBrowserOnStartField.checked = false
|
||||
}
|
||||
if (config.net && config.net.listen_to_network === false) {
|
||||
listenToNetworkField.checked = false
|
||||
}
|
||||
if (config.net && config.net.listen_port !== undefined) {
|
||||
listenPortField.value = config.net.listen_port
|
||||
}
|
||||
if ('test_sd2' in config) {
|
||||
testSD2Field.checked = config['test_sd2']
|
||||
}
|
||||
|
||||
let testSD2SettingEntry = getParameterSettingsEntry('test_sd2')
|
||||
testSD2SettingEntry.style.display = (config.update_branch === 'beta' ? '' : 'none')
|
||||
if (config.net && config.net.listen_to_network === false) {
|
||||
listenToNetworkField.checked = false
|
||||
}
|
||||
if (config.net && config.net.listen_port !== undefined) {
|
||||
listenPortField.value = config.net.listen_port
|
||||
}
|
||||
|
||||
console.log('get config status response', config)
|
||||
} catch (e) {
|
||||
@ -267,7 +291,6 @@ function getCurrentRenderDeviceSelection() {
|
||||
useCPUField.addEventListener('click', function() {
|
||||
let gpuSettingEntry = getParameterSettingsEntry('use_gpus')
|
||||
let autoPickGPUSettingEntry = getParameterSettingsEntry('auto_pick_gpus')
|
||||
console.log("hello", this.checked);
|
||||
if (this.checked) {
|
||||
gpuSettingEntry.style.display = 'none'
|
||||
autoPickGPUSettingEntry.style.display = 'none'
|
||||
@ -364,24 +387,25 @@ async function getDevices() {
|
||||
}
|
||||
|
||||
saveSettingsBtn.addEventListener('click', function() {
|
||||
let updateBranch = (useBetaChannelField.checked ? 'beta' : 'main')
|
||||
let updateBranch = (useBetaChannelField.checked ? 'beta' : 'main')
|
||||
|
||||
if (listenPortField.value == '') {
|
||||
alert('The network port field must not be empty.')
|
||||
} else if (listenPortField.value<1 || listenPortField.value>65535) {
|
||||
alert('The network port must be a number from 1 to 65535')
|
||||
} else {
|
||||
changeAppConfig({
|
||||
'render_devices': getCurrentRenderDeviceSelection(),
|
||||
'update_branch': updateBranch,
|
||||
'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
|
||||
'listen_to_network': listenToNetworkField.checked,
|
||||
'listen_port': listenPortField.value
|
||||
})
|
||||
}
|
||||
if (listenPortField.value == '') {
|
||||
alert('The network port field must not be empty.')
|
||||
} else if (listenPortField.value<1 || listenPortField.value>65535) {
|
||||
alert('The network port must be a number from 1 to 65535')
|
||||
} else {
|
||||
changeAppConfig({
|
||||
'render_devices': getCurrentRenderDeviceSelection(),
|
||||
'update_branch': updateBranch,
|
||||
'ui_open_browser_on_start': uiOpenBrowserOnStartField.checked,
|
||||
'listen_to_network': listenToNetworkField.checked,
|
||||
'listen_port': listenPortField.value,
|
||||
'test_sd2': testSD2Field.checked
|
||||
})
|
||||
}
|
||||
|
||||
saveSettingsBtn.classList.add('active')
|
||||
asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
|
||||
saveSettingsBtn.classList.add('active')
|
||||
asyncDelay(300).then(() => saveSettingsBtn.classList.remove('active'))
|
||||
})
|
||||
|
||||
getServerIPBtn.addEventListener('click', async function() {
|
||||
|
@ -1,17 +1,17 @@
|
||||
// https://gomakethings.com/finding-the-next-and-previous-sibling-elements-that-match-a-selector-with-vanilla-js/
|
||||
function getNextSibling(elem, selector) {
|
||||
// Get the next sibling element
|
||||
var sibling = elem.nextElementSibling
|
||||
// Get the next sibling element
|
||||
var sibling = elem.nextElementSibling
|
||||
|
||||
// If there's no selector, return the first sibling
|
||||
if (!selector) return sibling
|
||||
// If there's no selector, return the first sibling
|
||||
if (!selector) return sibling
|
||||
|
||||
// If the sibling matches our selector, use it
|
||||
// If not, jump to the next sibling and continue the loop
|
||||
while (sibling) {
|
||||
if (sibling.matches(selector)) return sibling
|
||||
sibling = sibling.nextElementSibling
|
||||
}
|
||||
// If the sibling matches our selector, use it
|
||||
// If not, jump to the next sibling and continue the loop
|
||||
while (sibling) {
|
||||
if (sibling.matches(selector)) return sibling
|
||||
sibling = sibling.nextElementSibling
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
84
ui/sd_internal/ddim_callback_sd2.patch
Normal file
84
ui/sd_internal/ddim_callback_sd2.patch
Normal file
@ -0,0 +1,84 @@
|
||||
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
|
||||
index 27ead0e..6215939 100644
|
||||
--- a/ldm/models/diffusion/ddim.py
|
||||
+++ b/ldm/models/diffusion/ddim.py
|
||||
@@ -100,7 +100,7 @@ class DDIMSampler(object):
|
||||
size = (batch_size, C, H, W)
|
||||
print(f'Data shape for DDIM sampling is {size}, eta {eta}')
|
||||
|
||||
- samples, intermediates = self.ddim_sampling(conditioning, size,
|
||||
+ samples = self.ddim_sampling(conditioning, size,
|
||||
callback=callback,
|
||||
img_callback=img_callback,
|
||||
quantize_denoised=quantize_x0,
|
||||
@@ -117,7 +117,8 @@ class DDIMSampler(object):
|
||||
dynamic_threshold=dynamic_threshold,
|
||||
ucg_schedule=ucg_schedule
|
||||
)
|
||||
- return samples, intermediates
|
||||
+ # return samples, intermediates
|
||||
+ yield from samples
|
||||
|
||||
@torch.no_grad()
|
||||
def ddim_sampling(self, cond, shape,
|
||||
@@ -168,14 +169,15 @@ class DDIMSampler(object):
|
||||
unconditional_conditioning=unconditional_conditioning,
|
||||
dynamic_threshold=dynamic_threshold)
|
||||
img, pred_x0 = outs
|
||||
- if callback: callback(i)
|
||||
- if img_callback: img_callback(pred_x0, i)
|
||||
+ if callback: yield from callback(i)
|
||||
+ if img_callback: yield from img_callback(pred_x0, i)
|
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1:
|
||||
intermediates['x_inter'].append(img)
|
||||
intermediates['pred_x0'].append(pred_x0)
|
||||
|
||||
- return img, intermediates
|
||||
+ # return img, intermediates
|
||||
+ yield from img_callback(pred_x0, len(iterator)-1)
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
||||
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
|
||||
index 7002a36..0951f39 100644
|
||||
--- a/ldm/models/diffusion/plms.py
|
||||
+++ b/ldm/models/diffusion/plms.py
|
||||
@@ -96,7 +96,7 @@ class PLMSSampler(object):
|
||||
size = (batch_size, C, H, W)
|
||||
print(f'Data shape for PLMS sampling is {size}')
|
||||
|
||||
- samples, intermediates = self.plms_sampling(conditioning, size,
|
||||
+ samples = self.plms_sampling(conditioning, size,
|
||||
callback=callback,
|
||||
img_callback=img_callback,
|
||||
quantize_denoised=quantize_x0,
|
||||
@@ -112,7 +112,8 @@ class PLMSSampler(object):
|
||||
unconditional_conditioning=unconditional_conditioning,
|
||||
dynamic_threshold=dynamic_threshold,
|
||||
)
|
||||
- return samples, intermediates
|
||||
+ #return samples, intermediates
|
||||
+ yield from samples
|
||||
|
||||
@torch.no_grad()
|
||||
def plms_sampling(self, cond, shape,
|
||||
@@ -165,14 +166,15 @@ class PLMSSampler(object):
|
||||
old_eps.append(e_t)
|
||||
if len(old_eps) >= 4:
|
||||
old_eps.pop(0)
|
||||
- if callback: callback(i)
|
||||
- if img_callback: img_callback(pred_x0, i)
|
||||
+ if callback: yield from callback(i)
|
||||
+ if img_callback: yield from img_callback(pred_x0, i)
|
||||
|
||||
if index % log_every_t == 0 or index == total_steps - 1:
|
||||
intermediates['x_inter'].append(img)
|
||||
intermediates['pred_x0'].append(pred_x0)
|
||||
|
||||
- return img, intermediates
|
||||
+ # return img, intermediates
|
||||
+ yield from img_callback(pred_x0, len(iterator)-1)
|
||||
|
||||
@torch.no_grad()
|
||||
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
@ -7,6 +7,7 @@ Notes:
|
||||
import json
|
||||
import os, re
|
||||
import traceback
|
||||
import queue
|
||||
import torch
|
||||
import numpy as np
|
||||
from gc import collect as gc_collect
|
||||
@ -21,13 +22,14 @@ from torch import autocast
|
||||
from contextlib import nullcontext
|
||||
from einops import rearrange, repeat
|
||||
from ldm.util import instantiate_from_config
|
||||
from optimizedSD.optimUtils import split_weighted_subprompts
|
||||
from transformers import logging
|
||||
|
||||
from gfpgan import GFPGANer
|
||||
from basicsr.archs.rrdbnet_arch import RRDBNet
|
||||
from realesrgan import RealESRGANer
|
||||
|
||||
from threading import Lock
|
||||
|
||||
import uuid
|
||||
|
||||
logging.set_verbosity_error()
|
||||
@ -35,7 +37,7 @@ logging.set_verbosity_error()
|
||||
# consts
|
||||
config_yaml = "optimizedSD/v1-inference.yaml"
|
||||
filename_regex = re.compile('[^a-zA-Z0-9]')
|
||||
force_gfpgan_to_cuda0 = True # workaround: gfpgan currently works only on cuda:0
|
||||
gfpgan_temp_device_lock = Lock() # workaround: gfpgan currently can only start on one device at a time.
|
||||
|
||||
# api stuff
|
||||
from sd_internal import device_manager
|
||||
@ -76,8 +78,24 @@ def thread_init(device):
|
||||
thread_data.force_full_precision = False
|
||||
thread_data.reduced_memory = True
|
||||
|
||||
thread_data.test_sd2 = isSD2()
|
||||
|
||||
device_manager.device_init(thread_data, device)
|
||||
|
||||
# temp hack, will remove soon
|
||||
def isSD2():
|
||||
try:
|
||||
SD_UI_DIR = os.getenv('SD_UI_PATH', None)
|
||||
CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '..', 'scripts'))
|
||||
config_json_path = os.path.join(CONFIG_DIR, 'config.json')
|
||||
if not os.path.exists(config_json_path):
|
||||
return False
|
||||
with open(config_json_path, 'r', encoding='utf-8') as f:
|
||||
config = json.load(f)
|
||||
return config.get('test_sd2', False)
|
||||
except Exception as e:
|
||||
return False
|
||||
|
||||
def load_model_ckpt():
|
||||
if not thread_data.ckpt_file: raise ValueError(f'Thread ckpt_file is undefined.')
|
||||
if not os.path.exists(thread_data.ckpt_file + '.ckpt'): raise FileNotFoundError(f'Cannot find {thread_data.ckpt_file}.ckpt')
|
||||
@ -92,6 +110,13 @@ def load_model_ckpt():
|
||||
thread_data.precision = 'full'
|
||||
|
||||
print('loading', thread_data.ckpt_file + '.ckpt', 'to device', thread_data.device, 'using precision', thread_data.precision)
|
||||
|
||||
if thread_data.test_sd2:
|
||||
load_model_ckpt_sd2()
|
||||
else:
|
||||
load_model_ckpt_sd1()
|
||||
|
||||
def load_model_ckpt_sd1():
|
||||
sd = load_model_from_config(thread_data.ckpt_file + '.ckpt')
|
||||
li, lo = [], []
|
||||
for key, value in sd.items():
|
||||
@ -185,6 +210,38 @@ def load_model_ckpt():
|
||||
modelFS.device: {thread_data.modelFS.device}
|
||||
using precision: {thread_data.precision}''')
|
||||
|
||||
def load_model_ckpt_sd2():
|
||||
config_file = 'configs/stable-diffusion/v2-inference-v.yaml' if 'sd2_' in thread_data.ckpt_file else "configs/stable-diffusion/v1-inference.yaml"
|
||||
config = OmegaConf.load(config_file)
|
||||
verbose = False
|
||||
|
||||
sd = load_model_from_config(thread_data.ckpt_file + '.ckpt')
|
||||
|
||||
thread_data.model = instantiate_from_config(config.model)
|
||||
m, u = thread_data.model.load_state_dict(sd, strict=False)
|
||||
if len(m) > 0 and verbose:
|
||||
print("missing keys:")
|
||||
print(m)
|
||||
if len(u) > 0 and verbose:
|
||||
print("unexpected keys:")
|
||||
print(u)
|
||||
|
||||
thread_data.model.to(thread_data.device)
|
||||
thread_data.model.eval()
|
||||
del sd
|
||||
|
||||
if thread_data.device != "cpu" and thread_data.precision == "autocast":
|
||||
thread_data.model.half()
|
||||
thread_data.model_is_half = True
|
||||
thread_data.model_fs_is_half = True
|
||||
else:
|
||||
thread_data.model_is_half = False
|
||||
thread_data.model_fs_is_half = False
|
||||
|
||||
print(f'''loaded model
|
||||
model file: {thread_data.ckpt_file}.ckpt
|
||||
using precision: {thread_data.precision}''')
|
||||
|
||||
def unload_filters():
|
||||
if thread_data.model_gfpgan is not None:
|
||||
if thread_data.device != 'cpu': thread_data.model_gfpgan.gfpgan.to('cpu')
|
||||
@ -204,10 +261,11 @@ def unload_models():
|
||||
if thread_data.model is not None:
|
||||
print('Unloading models...')
|
||||
if thread_data.device != 'cpu':
|
||||
thread_data.modelFS.to('cpu')
|
||||
thread_data.modelCS.to('cpu')
|
||||
thread_data.model.model1.to("cpu")
|
||||
thread_data.model.model2.to("cpu")
|
||||
if not thread_data.test_sd2:
|
||||
thread_data.modelFS.to('cpu')
|
||||
thread_data.modelCS.to('cpu')
|
||||
thread_data.model.model1.to("cpu")
|
||||
thread_data.model.model2.to("cpu")
|
||||
|
||||
del thread_data.model
|
||||
del thread_data.modelCS
|
||||
@ -253,12 +311,6 @@ def move_to_cpu(model):
|
||||
|
||||
def load_model_gfpgan():
|
||||
if thread_data.gfpgan_file is None: raise ValueError(f'Thread gfpgan_file is undefined.')
|
||||
|
||||
# hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
|
||||
from facexlib.detection import retinaface
|
||||
retinaface.device = torch.device(thread_data.device)
|
||||
print('forced retinaface.device to', thread_data.device)
|
||||
|
||||
model_path = thread_data.gfpgan_file + ".pth"
|
||||
thread_data.model_gfpgan = GFPGANer(device=torch.device(thread_data.device), model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
|
||||
print('loaded', thread_data.gfpgan_file, 'to', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
|
||||
@ -314,15 +366,23 @@ def apply_filters(filter_name, image_data, model_path=None):
|
||||
image_data.to(thread_data.device)
|
||||
|
||||
if filter_name == 'gfpgan':
|
||||
if model_path is not None and model_path != thread_data.gfpgan_file:
|
||||
thread_data.gfpgan_file = model_path
|
||||
load_model_gfpgan()
|
||||
elif not thread_data.model_gfpgan:
|
||||
load_model_gfpgan()
|
||||
if thread_data.model_gfpgan is None: raise Exception('Model "gfpgan" not loaded.')
|
||||
print('enhance with', thread_data.gfpgan_file, 'on', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
|
||||
_, _, output = thread_data.model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||||
image_data = output[:,:,::-1]
|
||||
# This lock is only ever used here. No need to use timeout for the request. Should never deadlock.
|
||||
with gfpgan_temp_device_lock: # Wait for any other devices to complete before starting.
|
||||
# hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
|
||||
from facexlib.detection import retinaface
|
||||
retinaface.device = torch.device(thread_data.device)
|
||||
print('forced retinaface.device to', thread_data.device)
|
||||
|
||||
if model_path is not None and model_path != thread_data.gfpgan_file:
|
||||
thread_data.gfpgan_file = model_path
|
||||
load_model_gfpgan()
|
||||
elif not thread_data.model_gfpgan:
|
||||
load_model_gfpgan()
|
||||
if thread_data.model_gfpgan is None: raise Exception('Model "gfpgan" not loaded.')
|
||||
|
||||
print('enhance with', thread_data.gfpgan_file, 'on', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
|
||||
_, _, output = thread_data.model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
|
||||
image_data = output[:,:,::-1]
|
||||
|
||||
if filter_name == 'real_esrgan':
|
||||
if model_path is not None and model_path != thread_data.real_esrgan_file:
|
||||
@ -337,45 +397,73 @@ def apply_filters(filter_name, image_data, model_path=None):
|
||||
|
||||
return image_data
|
||||
|
||||
def mk_img(req: Request):
|
||||
def is_model_reload_necessary(req: Request):
|
||||
# custom model support:
|
||||
# the req.use_stable_diffusion_model needs to be a valid path
|
||||
# to the ckpt file (without the extension).
|
||||
if not os.path.exists(req.use_stable_diffusion_model + '.ckpt'): raise FileNotFoundError(f'Cannot find {req.use_stable_diffusion_model}.ckpt')
|
||||
|
||||
needs_model_reload = False
|
||||
if not thread_data.model or thread_data.ckpt_file != req.use_stable_diffusion_model or thread_data.vae_file != req.use_vae_model:
|
||||
thread_data.ckpt_file = req.use_stable_diffusion_model
|
||||
thread_data.vae_file = req.use_vae_model
|
||||
needs_model_reload = True
|
||||
|
||||
if thread_data.device != 'cpu':
|
||||
if (thread_data.precision == 'autocast' and (req.use_full_precision or not thread_data.model_is_half)) or \
|
||||
(thread_data.precision == 'full' and not req.use_full_precision and not thread_data.force_full_precision):
|
||||
thread_data.precision = 'full' if req.use_full_precision else 'autocast'
|
||||
needs_model_reload = True
|
||||
|
||||
return needs_model_reload
|
||||
|
||||
def reload_model():
|
||||
unload_models()
|
||||
unload_filters()
|
||||
load_model_ckpt()
|
||||
|
||||
def mk_img(req: Request, data_queue: queue.Queue, task_temp_images: list, step_callback):
|
||||
try:
|
||||
yield from do_mk_img(req)
|
||||
return do_mk_img(req, data_queue, task_temp_images, step_callback)
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
|
||||
if thread_data.device != 'cpu':
|
||||
if thread_data.device != 'cpu' and not thread_data.test_sd2:
|
||||
thread_data.modelFS.to('cpu')
|
||||
thread_data.modelCS.to('cpu')
|
||||
thread_data.model.model1.to("cpu")
|
||||
thread_data.model.model2.to("cpu")
|
||||
|
||||
gc() # Release from memory.
|
||||
yield json.dumps({
|
||||
data_queue.put(json.dumps({
|
||||
"status": 'failed',
|
||||
"detail": str(e)
|
||||
})
|
||||
}))
|
||||
raise e
|
||||
|
||||
def update_temp_img(req, x_samples):
|
||||
def update_temp_img(req, x_samples, task_temp_images: list):
|
||||
partial_images = []
|
||||
for i in range(req.num_outputs):
|
||||
x_sample_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
if thread_data.test_sd2:
|
||||
x_sample_ddim = thread_data.model.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
else:
|
||||
x_sample_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
x_sample = torch.clamp((x_sample_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
img = Image.fromarray(x_sample)
|
||||
buf = BytesIO()
|
||||
img.save(buf, format='JPEG')
|
||||
buf.seek(0)
|
||||
buf = img_to_buffer(img, output_format='JPEG')
|
||||
|
||||
del img, x_sample, x_sample_ddim
|
||||
# don't delete x_samples, it is used in the code that called this callback
|
||||
|
||||
thread_data.temp_images[str(req.session_id) + '/' + str(i)] = buf
|
||||
task_temp_images[i] = buf
|
||||
partial_images.append({'path': f'/image/tmp/{req.session_id}/{i}'})
|
||||
return partial_images
|
||||
|
||||
# Build and return the apropriate generator for do_mk_img
|
||||
def get_image_progress_generator(req, extra_props=None):
|
||||
def get_image_progress_generator(req, data_queue: queue.Queue, task_temp_images: list, step_callback, extra_props=None):
|
||||
if not req.stream_progress_updates:
|
||||
def empty_callback(x_samples, i): return x_samples
|
||||
return empty_callback
|
||||
@ -394,15 +482,17 @@ def get_image_progress_generator(req, extra_props=None):
|
||||
progress.update(extra_props)
|
||||
|
||||
if req.stream_image_progress and i % 5 == 0:
|
||||
progress['output'] = update_temp_img(req, x_samples)
|
||||
progress['output'] = update_temp_img(req, x_samples, task_temp_images)
|
||||
|
||||
yield json.dumps(progress)
|
||||
data_queue.put(json.dumps(progress))
|
||||
|
||||
step_callback()
|
||||
|
||||
if thread_data.stop_processing:
|
||||
raise UserInitiatedStop("User requested that we stop processing")
|
||||
return img_callback
|
||||
|
||||
def do_mk_img(req: Request):
|
||||
def do_mk_img(req: Request, data_queue: queue.Queue, task_temp_images: list, step_callback):
|
||||
thread_data.stop_processing = False
|
||||
|
||||
res = Response()
|
||||
@ -411,29 +501,7 @@ def do_mk_img(req: Request):
|
||||
|
||||
thread_data.temp_images.clear()
|
||||
|
||||
# custom model support:
|
||||
# the req.use_stable_diffusion_model needs to be a valid path
|
||||
# to the ckpt file (without the extension).
|
||||
if not os.path.exists(req.use_stable_diffusion_model + '.ckpt'): raise FileNotFoundError(f'Cannot find {req.use_stable_diffusion_model}.ckpt')
|
||||
|
||||
needs_model_reload = False
|
||||
if not thread_data.model or thread_data.ckpt_file != req.use_stable_diffusion_model or thread_data.vae_file != req.use_vae_model:
|
||||
thread_data.ckpt_file = req.use_stable_diffusion_model
|
||||
thread_data.vae_file = req.use_vae_model
|
||||
needs_model_reload = True
|
||||
|
||||
if thread_data.device != 'cpu':
|
||||
if (thread_data.precision == 'autocast' and (req.use_full_precision or not thread_data.model_is_half)) or \
|
||||
(thread_data.precision == 'full' and not req.use_full_precision and not thread_data.force_full_precision):
|
||||
thread_data.precision = 'full' if req.use_full_precision else 'autocast'
|
||||
needs_model_reload = True
|
||||
|
||||
if needs_model_reload:
|
||||
unload_models()
|
||||
unload_filters()
|
||||
load_model_ckpt()
|
||||
|
||||
if thread_data.turbo != req.turbo:
|
||||
if thread_data.turbo != req.turbo and not thread_data.test_sd2:
|
||||
thread_data.turbo = req.turbo
|
||||
thread_data.model.turbo = req.turbo
|
||||
|
||||
@ -478,10 +546,14 @@ def do_mk_img(req: Request):
|
||||
if thread_data.device != "cpu" and thread_data.precision == "autocast":
|
||||
init_image = init_image.half()
|
||||
|
||||
thread_data.modelFS.to(thread_data.device)
|
||||
if not thread_data.test_sd2:
|
||||
thread_data.modelFS.to(thread_data.device)
|
||||
|
||||
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
|
||||
init_latent = thread_data.modelFS.get_first_stage_encoding(thread_data.modelFS.encode_first_stage(init_image)) # move to latent space
|
||||
if thread_data.test_sd2:
|
||||
init_latent = thread_data.model.get_first_stage_encoding(thread_data.model.encode_first_stage(init_image)) # move to latent space
|
||||
else:
|
||||
init_latent = thread_data.modelFS.get_first_stage_encoding(thread_data.modelFS.encode_first_stage(init_image)) # move to latent space
|
||||
|
||||
if req.mask is not None:
|
||||
mask = load_mask(req.mask, req.width, req.height, init_latent.shape[2], init_latent.shape[3], True).to(thread_data.device)
|
||||
@ -493,7 +565,8 @@ def do_mk_img(req: Request):
|
||||
|
||||
# Send to CPU and wait until complete.
|
||||
# wait_model_move_to(thread_data.modelFS, 'cpu')
|
||||
move_to_cpu(thread_data.modelFS)
|
||||
if not thread_data.test_sd2:
|
||||
move_to_cpu(thread_data.modelFS)
|
||||
|
||||
assert 0. <= req.prompt_strength <= 1., 'can only work with strength in [0.0, 1.0]'
|
||||
t_enc = int(req.prompt_strength * req.num_inference_steps)
|
||||
@ -509,11 +582,14 @@ def do_mk_img(req: Request):
|
||||
for prompts in tqdm(data, desc="data"):
|
||||
|
||||
with precision_scope("cuda"):
|
||||
if thread_data.reduced_memory:
|
||||
if thread_data.reduced_memory and not thread_data.test_sd2:
|
||||
thread_data.modelCS.to(thread_data.device)
|
||||
uc = None
|
||||
if req.guidance_scale != 1.0:
|
||||
uc = thread_data.modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
|
||||
if thread_data.test_sd2:
|
||||
uc = thread_data.model.get_learned_conditioning(batch_size * [req.negative_prompt])
|
||||
else:
|
||||
uc = thread_data.modelCS.get_learned_conditioning(batch_size * [req.negative_prompt])
|
||||
if isinstance(prompts, tuple):
|
||||
prompts = list(prompts)
|
||||
|
||||
@ -526,15 +602,21 @@ def do_mk_img(req: Request):
|
||||
weight = weights[i]
|
||||
# if not skip_normalize:
|
||||
weight = weight / totalWeight
|
||||
c = torch.add(c, thread_data.modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
|
||||
if thread_data.test_sd2:
|
||||
c = torch.add(c, thread_data.model.get_learned_conditioning(subprompts[i]), alpha=weight)
|
||||
else:
|
||||
c = torch.add(c, thread_data.modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
|
||||
else:
|
||||
c = thread_data.modelCS.get_learned_conditioning(prompts)
|
||||
if thread_data.test_sd2:
|
||||
c = thread_data.model.get_learned_conditioning(prompts)
|
||||
else:
|
||||
c = thread_data.modelCS.get_learned_conditioning(prompts)
|
||||
|
||||
if thread_data.reduced_memory:
|
||||
if thread_data.reduced_memory and not thread_data.test_sd2:
|
||||
thread_data.modelFS.to(thread_data.device)
|
||||
|
||||
n_steps = req.num_inference_steps if req.init_image is None else t_enc
|
||||
img_callback = get_image_progress_generator(req, {"total_steps": n_steps})
|
||||
img_callback = get_image_progress_generator(req, data_queue, task_temp_images, step_callback, {"total_steps": n_steps})
|
||||
|
||||
# run the handler
|
||||
try:
|
||||
@ -542,14 +624,7 @@ def do_mk_img(req: Request):
|
||||
if handler == _txt2img:
|
||||
x_samples = _txt2img(req.width, req.height, req.num_outputs, req.num_inference_steps, req.guidance_scale, None, opt_C, opt_f, opt_ddim_eta, c, uc, opt_seed, img_callback, mask, req.sampler)
|
||||
else:
|
||||
x_samples = _img2img(init_latent, t_enc, batch_size, req.guidance_scale, c, uc, req.num_inference_steps, opt_ddim_eta, opt_seed, img_callback, mask)
|
||||
|
||||
if req.stream_progress_updates:
|
||||
yield from x_samples
|
||||
if hasattr(thread_data, 'partial_x_samples'):
|
||||
if thread_data.partial_x_samples is not None:
|
||||
x_samples = thread_data.partial_x_samples
|
||||
del thread_data.partial_x_samples
|
||||
x_samples = _img2img(init_latent, t_enc, batch_size, req.guidance_scale, c, uc, req.num_inference_steps, opt_ddim_eta, opt_seed, img_callback, mask, opt_C, req.height, req.width, opt_f)
|
||||
except UserInitiatedStop:
|
||||
if not hasattr(thread_data, 'partial_x_samples'):
|
||||
continue
|
||||
@ -562,7 +637,10 @@ def do_mk_img(req: Request):
|
||||
print("decoding images")
|
||||
img_data = [None] * batch_size
|
||||
for i in range(batch_size):
|
||||
x_samples_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
if thread_data.test_sd2:
|
||||
x_samples_ddim = thread_data.model.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
else:
|
||||
x_samples_ddim = thread_data.modelFS.decode_first_stage(x_samples[i].unsqueeze(0))
|
||||
x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
|
||||
x_sample = x_sample.astype(np.uint8)
|
||||
@ -591,9 +669,11 @@ def do_mk_img(req: Request):
|
||||
save_metadata(meta_out_path, req, prompts[0], opt_seed)
|
||||
|
||||
if return_orig_img:
|
||||
img_str = img_to_base64_str(img, req.output_format)
|
||||
img_buffer = img_to_buffer(img, req.output_format)
|
||||
img_str = buffer_to_base64_str(img_buffer, req.output_format)
|
||||
res_image_orig = ResponseImage(data=img_str, seed=opt_seed)
|
||||
res.images.append(res_image_orig)
|
||||
task_temp_images[i] = img_buffer
|
||||
|
||||
if req.save_to_disk_path is not None:
|
||||
res_image_orig.path_abs = img_out_path
|
||||
@ -609,9 +689,11 @@ def do_mk_img(req: Request):
|
||||
filters_applied.append(req.use_upscale)
|
||||
if (len(filters_applied) > 0):
|
||||
filtered_image = Image.fromarray(img_data[i])
|
||||
filtered_img_data = img_to_base64_str(filtered_image, req.output_format)
|
||||
filtered_buffer = img_to_buffer(filtered_image, req.output_format)
|
||||
filtered_img_data = buffer_to_base64_str(filtered_buffer, req.output_format)
|
||||
response_image = ResponseImage(data=filtered_img_data, seed=opt_seed)
|
||||
res.images.append(response_image)
|
||||
task_temp_images[i] = filtered_buffer
|
||||
if req.save_to_disk_path is not None:
|
||||
filtered_img_out_path = get_base_path(req.save_to_disk_path, req.session_id, prompts[0], img_id, req.output_format, "_".join(filters_applied))
|
||||
save_image(filtered_image, filtered_img_out_path)
|
||||
@ -622,14 +704,18 @@ def do_mk_img(req: Request):
|
||||
|
||||
# if thread_data.reduced_memory:
|
||||
# unload_filters()
|
||||
move_to_cpu(thread_data.modelFS)
|
||||
if not thread_data.test_sd2:
|
||||
move_to_cpu(thread_data.modelFS)
|
||||
del img_data
|
||||
gc()
|
||||
if thread_data.device != 'cpu':
|
||||
print(f'memory_final = {round(torch.cuda.memory_allocated(thread_data.device) / 1e6, 2)}Mb')
|
||||
|
||||
print('Task completed')
|
||||
yield json.dumps(res.json())
|
||||
res = res.json()
|
||||
data_queue.put(json.dumps(res))
|
||||
|
||||
return res
|
||||
|
||||
def save_image(img, img_out_path):
|
||||
try:
|
||||
@ -664,51 +750,109 @@ def _txt2img(opt_W, opt_H, opt_n_samples, opt_ddim_steps, opt_scale, start_code,
|
||||
# Send to CPU and wait until complete.
|
||||
# wait_model_move_to(thread_data.modelCS, 'cpu')
|
||||
|
||||
move_to_cpu(thread_data.modelCS)
|
||||
if not thread_data.test_sd2:
|
||||
move_to_cpu(thread_data.modelCS)
|
||||
|
||||
if sampler_name == 'ddim':
|
||||
thread_data.model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
|
||||
if thread_data.test_sd2 and sampler_name not in ('plms', 'ddim'):
|
||||
raise Exception('Only plms and ddim samplers are supported right now, in SD 2.0')
|
||||
|
||||
samples_ddim = thread_data.model.sample(
|
||||
S=opt_ddim_steps,
|
||||
conditioning=c,
|
||||
seed=opt_seed,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt_ddim_eta,
|
||||
x_T=start_code,
|
||||
img_callback=img_callback,
|
||||
mask=mask,
|
||||
sampler = sampler_name,
|
||||
)
|
||||
yield from samples_ddim
|
||||
|
||||
def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask):
|
||||
# samples, _ = sampler.sample(S=opt.steps,
|
||||
# conditioning=c,
|
||||
# batch_size=opt.n_samples,
|
||||
# shape=shape,
|
||||
# verbose=False,
|
||||
# unconditional_guidance_scale=opt.scale,
|
||||
# unconditional_conditioning=uc,
|
||||
# eta=opt.ddim_eta,
|
||||
# x_T=start_code)
|
||||
|
||||
if thread_data.test_sd2:
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
from ldm.models.diffusion.plms import PLMSSampler
|
||||
|
||||
shape = [opt_C, opt_H // opt_f, opt_W // opt_f]
|
||||
|
||||
if sampler_name == 'plms':
|
||||
sampler = PLMSSampler(thread_data.model)
|
||||
elif sampler_name == 'ddim':
|
||||
sampler = DDIMSampler(thread_data.model)
|
||||
|
||||
sampler.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
|
||||
|
||||
|
||||
samples_ddim, intermediates = sampler.sample(
|
||||
S=opt_ddim_steps,
|
||||
conditioning=c,
|
||||
batch_size=opt_n_samples,
|
||||
seed=opt_seed,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt_ddim_eta,
|
||||
x_T=start_code,
|
||||
img_callback=img_callback,
|
||||
mask=mask,
|
||||
sampler = sampler_name,
|
||||
)
|
||||
else:
|
||||
if sampler_name == 'ddim':
|
||||
thread_data.model.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
|
||||
|
||||
samples_ddim = thread_data.model.sample(
|
||||
S=opt_ddim_steps,
|
||||
conditioning=c,
|
||||
seed=opt_seed,
|
||||
shape=shape,
|
||||
verbose=False,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
eta=opt_ddim_eta,
|
||||
x_T=start_code,
|
||||
img_callback=img_callback,
|
||||
mask=mask,
|
||||
sampler = sampler_name,
|
||||
)
|
||||
return samples_ddim
|
||||
|
||||
def _img2img(init_latent, t_enc, batch_size, opt_scale, c, uc, opt_ddim_steps, opt_ddim_eta, opt_seed, img_callback, mask, opt_C=1, opt_H=1, opt_W=1, opt_f=1):
|
||||
# encode (scaled latent)
|
||||
z_enc = thread_data.model.stochastic_encode(
|
||||
init_latent,
|
||||
torch.tensor([t_enc] * batch_size).to(thread_data.device),
|
||||
opt_seed,
|
||||
opt_ddim_eta,
|
||||
opt_ddim_steps,
|
||||
)
|
||||
x_T = None if mask is None else init_latent
|
||||
|
||||
# decode it
|
||||
samples_ddim = thread_data.model.sample(
|
||||
t_enc,
|
||||
c,
|
||||
z_enc,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
img_callback=img_callback,
|
||||
mask=mask,
|
||||
x_T=x_T,
|
||||
sampler = 'ddim'
|
||||
)
|
||||
yield from samples_ddim
|
||||
if thread_data.test_sd2:
|
||||
from ldm.models.diffusion.ddim import DDIMSampler
|
||||
|
||||
sampler = DDIMSampler(thread_data.model)
|
||||
|
||||
sampler.make_schedule(ddim_num_steps=opt_ddim_steps, ddim_eta=opt_ddim_eta, verbose=False)
|
||||
|
||||
z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc] * batch_size).to(thread_data.device))
|
||||
|
||||
samples_ddim = sampler.decode(z_enc, c, t_enc, unconditional_guidance_scale=opt_scale,unconditional_conditioning=uc, img_callback=img_callback)
|
||||
|
||||
else:
|
||||
z_enc = thread_data.model.stochastic_encode(
|
||||
init_latent,
|
||||
torch.tensor([t_enc] * batch_size).to(thread_data.device),
|
||||
opt_seed,
|
||||
opt_ddim_eta,
|
||||
opt_ddim_steps,
|
||||
)
|
||||
|
||||
# decode it
|
||||
samples_ddim = thread_data.model.sample(
|
||||
t_enc,
|
||||
c,
|
||||
z_enc,
|
||||
unconditional_guidance_scale=opt_scale,
|
||||
unconditional_conditioning=uc,
|
||||
img_callback=img_callback,
|
||||
mask=mask,
|
||||
x_T=x_T,
|
||||
sampler = 'ddim'
|
||||
)
|
||||
return samples_ddim
|
||||
|
||||
def gc():
|
||||
gc_collect()
|
||||
@ -776,8 +920,16 @@ def load_mask(mask_str, h0, w0, newH, newW, invert=False):
|
||||
|
||||
# https://stackoverflow.com/a/61114178
|
||||
def img_to_base64_str(img, output_format="PNG"):
|
||||
buffered = img_to_buffer(img, output_format)
|
||||
return buffer_to_base64_str(buffered, output_format)
|
||||
|
||||
def img_to_buffer(img, output_format="PNG"):
|
||||
buffered = BytesIO()
|
||||
img.save(buffered, format=output_format)
|
||||
buffered.seek(0)
|
||||
return buffered
|
||||
|
||||
def buffer_to_base64_str(buffered, output_format="PNG"):
|
||||
buffered.seek(0)
|
||||
img_byte = buffered.getvalue()
|
||||
mime_type = "image/png" if output_format.lower() == "png" else "image/jpeg"
|
||||
@ -795,3 +947,48 @@ def base64_str_to_img(img_str):
|
||||
buffered = base64_str_to_buffer(img_str)
|
||||
img = Image.open(buffered)
|
||||
return img
|
||||
|
||||
def split_weighted_subprompts(text):
|
||||
"""
|
||||
grabs all text up to the first occurrence of ':'
|
||||
uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
|
||||
if ':' has no value defined, defaults to 1.0
|
||||
repeats until no text remaining
|
||||
"""
|
||||
remaining = len(text)
|
||||
prompts = []
|
||||
weights = []
|
||||
while remaining > 0:
|
||||
if ":" in text:
|
||||
idx = text.index(":") # first occurrence from start
|
||||
# grab up to index as sub-prompt
|
||||
prompt = text[:idx]
|
||||
remaining -= idx
|
||||
# remove from main text
|
||||
text = text[idx+1:]
|
||||
# find value for weight
|
||||
if " " in text:
|
||||
idx = text.index(" ") # first occurence
|
||||
else: # no space, read to end
|
||||
idx = len(text)
|
||||
if idx != 0:
|
||||
try:
|
||||
weight = float(text[:idx])
|
||||
except: # couldn't treat as float
|
||||
print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
|
||||
weight = 1.0
|
||||
else: # no value found
|
||||
weight = 1.0
|
||||
# remove from main text
|
||||
remaining -= idx
|
||||
text = text[idx+1:]
|
||||
# append the sub-prompt and its weight
|
||||
prompts.append(prompt)
|
||||
weights.append(weight)
|
||||
else: # no : found
|
||||
if len(text) > 0: # there is still text though
|
||||
# take remainder as weight 1
|
||||
prompts.append(text)
|
||||
weights.append(1.0)
|
||||
remaining = 0
|
||||
return prompts, weights
|
||||
|
@ -283,45 +283,26 @@ def thread_render(device):
|
||||
print(f'Session {task.request.session_id} starting task {id(task)} on {runtime.thread_data.device_name}')
|
||||
if not task.lock.acquire(blocking=False): raise Exception('Got locked task from queue.')
|
||||
try:
|
||||
if runtime.thread_data.device == 'cpu' and is_alive() > 1:
|
||||
# CPU is not the only device. Keep track of active time to unload resources later.
|
||||
runtime.thread_data.lastActive = time.time()
|
||||
# Open data generator.
|
||||
res = runtime.mk_img(task.request)
|
||||
if current_model_path == task.request.use_stable_diffusion_model:
|
||||
current_state = ServerStates.Rendering
|
||||
else:
|
||||
if runtime.is_model_reload_necessary(task.request):
|
||||
current_state = ServerStates.LoadingModel
|
||||
# Start reading from generator.
|
||||
dataQueue = None
|
||||
if task.request.stream_progress_updates:
|
||||
dataQueue = task.buffer_queue
|
||||
for result in res:
|
||||
if current_state == ServerStates.LoadingModel:
|
||||
current_state = ServerStates.Rendering
|
||||
current_model_path = task.request.use_stable_diffusion_model
|
||||
current_vae_path = task.request.use_vae_model
|
||||
runtime.reload_model()
|
||||
current_model_path = task.request.use_stable_diffusion_model
|
||||
current_vae_path = task.request.use_vae_model
|
||||
|
||||
def step_callback():
|
||||
global current_state_error
|
||||
|
||||
if isinstance(current_state_error, SystemExit) or isinstance(current_state_error, StopAsyncIteration) or isinstance(task.error, StopAsyncIteration):
|
||||
runtime.thread_data.stop_processing = True
|
||||
if isinstance(current_state_error, StopAsyncIteration):
|
||||
task.error = current_state_error
|
||||
current_state_error = None
|
||||
print(f'Session {task.request.session_id} sent cancel signal for task {id(task)}')
|
||||
if dataQueue:
|
||||
dataQueue.put(result)
|
||||
if isinstance(result, str):
|
||||
result = json.loads(result)
|
||||
task.response = result
|
||||
if 'output' in result:
|
||||
for out_obj in result['output']:
|
||||
if 'path' in out_obj:
|
||||
img_id = out_obj['path'][out_obj['path'].rindex('/') + 1:]
|
||||
task.temp_images[int(img_id)] = runtime.thread_data.temp_images[out_obj['path'][11:]]
|
||||
elif 'data' in out_obj:
|
||||
buf = runtime.base64_str_to_buffer(out_obj['data'])
|
||||
task.temp_images[result['output'].index(out_obj)] = buf
|
||||
# Before looping back to the generator, mark cache as still alive.
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
|
||||
task_cache.keep(task.request.session_id, TASK_TTL)
|
||||
|
||||
current_state = ServerStates.Rendering
|
||||
task.response = runtime.mk_img(task.request, task.buffer_queue, task.temp_images, step_callback)
|
||||
except Exception as e:
|
||||
task.error = e
|
||||
print(traceback.format_exc())
|
||||
|
32
ui/server.py
32
ui/server.py
@ -117,6 +117,8 @@ def setConfig(config):
|
||||
bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
|
||||
config_bat.append(f"@set SD_UI_BIND_IP={bind_ip}")
|
||||
|
||||
config_bat.append(f"@set test_sd2={'Y' if config.get('test_sd2', False) else 'N'}")
|
||||
|
||||
if len(config_bat) > 0:
|
||||
with open(config_bat_path, 'w', encoding='utf-8') as f:
|
||||
f.write('\r\n'.join(config_bat))
|
||||
@ -134,6 +136,8 @@ def setConfig(config):
|
||||
bind_ip = '0.0.0.0' if config['net']['listen_to_network'] else '127.0.0.1'
|
||||
config_sh.append(f"export SD_UI_BIND_IP={bind_ip}")
|
||||
|
||||
config_sh.append(f"export test_sd2=\"{'Y' if config.get('test_sd2', False) else 'N'}\"")
|
||||
|
||||
if len(config_sh) > 1:
|
||||
with open(config_sh_path, 'w', encoding='utf-8') as f:
|
||||
f.write('\n'.join(config_sh))
|
||||
@ -141,12 +145,19 @@ def setConfig(config):
|
||||
print(traceback.format_exc())
|
||||
|
||||
def resolve_model_to_use(model_name:str, model_type:str, model_dir:str, model_extensions:list, default_models=[]):
|
||||
config = getConfig()
|
||||
|
||||
model_dirs = [os.path.join(MODELS_DIR, model_dir), SD_DIR]
|
||||
if not model_name: # When None try user configured model.
|
||||
config = getConfig()
|
||||
# config = getConfig()
|
||||
if 'model' in config and model_type in config['model']:
|
||||
model_name = config['model'][model_type]
|
||||
if model_name:
|
||||
is_sd2 = config.get('test_sd2', False)
|
||||
if model_name.startswith('sd2_') and not is_sd2: # temp hack, until SD2 is unified with 1.4
|
||||
print('ERROR: Cannot use SD 2.0 models with SD 1.0 code. Using the sd-v1-4 model instead!')
|
||||
model_name = 'sd-v1-4'
|
||||
|
||||
# Check models directory
|
||||
models_dir_path = os.path.join(MODELS_DIR, model_dir, model_name)
|
||||
for model_extension in model_extensions:
|
||||
@ -189,6 +200,7 @@ class SetAppConfigRequest(BaseModel):
|
||||
ui_open_browser_on_start: bool = None
|
||||
listen_to_network: bool = None
|
||||
listen_port: int = None
|
||||
test_sd2: bool = None
|
||||
|
||||
@app.post('/app_config')
|
||||
async def setAppConfig(req : SetAppConfigRequest):
|
||||
@ -209,6 +221,8 @@ async def setAppConfig(req : SetAppConfigRequest):
|
||||
if 'net' not in config:
|
||||
config['net'] = {}
|
||||
config['net']['listen_port'] = int(req.listen_port)
|
||||
if req.test_sd2 is not None:
|
||||
config['test_sd2'] = req.test_sd2
|
||||
try:
|
||||
setConfig(config)
|
||||
|
||||
@ -231,9 +245,9 @@ def is_malicious_model(file_path):
|
||||
return False
|
||||
except Exception as e:
|
||||
print('error while scanning', file_path, 'error:', e)
|
||||
|
||||
return False
|
||||
|
||||
known_models = {}
|
||||
def getModels():
|
||||
models = {
|
||||
'active': {
|
||||
@ -256,9 +270,14 @@ def getModels():
|
||||
if not file.endswith(model_extension):
|
||||
continue
|
||||
|
||||
if is_malicious_model(os.path.join(models_dir, file)):
|
||||
models['scan-error'] = file
|
||||
return
|
||||
model_path = os.path.join(models_dir, file)
|
||||
mtime = os.path.getmtime(model_path)
|
||||
mod_time = known_models[model_path] if model_path in known_models else -1
|
||||
if mod_time != mtime:
|
||||
if is_malicious_model(model_path):
|
||||
models['scan-error'] = file
|
||||
return
|
||||
known_models[model_path] = mtime
|
||||
|
||||
model_name = file[:-len(model_extension)]
|
||||
models['options'][model_type].append(model_name)
|
||||
@ -440,6 +459,9 @@ class LogSuppressFilter(logging.Filter):
|
||||
return True
|
||||
logging.getLogger('uvicorn.access').addFilter(LogSuppressFilter())
|
||||
|
||||
# Check models and prepare cache for UI open
|
||||
getModels()
|
||||
|
||||
# Start the task_manager
|
||||
task_manager.default_model_to_load = resolve_ckpt_to_use()
|
||||
task_manager.default_vae_to_load = resolve_vae_to_use()
|
||||
|
Loading…
Reference in New Issue
Block a user