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Merge branch 'beta' into serverip

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27
3rd-PARTY-LICENSES Normal file
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@ -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.

8
CHANGES.md
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@ -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.

12
scripts/Developer Console.cmd
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@ -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

9
scripts/developer_console.sh
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@ -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]}")

16
scripts/on_sd_start.bat
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@ -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
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@ -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

4
ui/index.html
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@ -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
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21
ui/media/css/main.css
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@ -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;

1
ui/media/js/auto-save.js
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@ -35,6 +35,7 @@ const SETTINGS_IDS_LIST = [
"sound_toggle",
"turbo",
"use_full_precision",
"confirm_dangerous_actions",
"auto_save_settings"
]

22
ui/media/js/image-modifiers.js
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@ -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
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File diff suppressed because one or more lines are too long

44
ui/media/js/main.js
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@ -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 {
$.confirm({ theme: 'supervan',
title: prompt,
content: 'Tip: To skip this dialog, use shift-click or disable the setting "Confirm dangerous actions" in the systems setting.',
buttons: {
yes: () => { fn(e) },
cancel: () => {}
}
});
}
}
function logMsg(msg, level, outputMsg) {
if (outputMsg.hasChildNodes()) {
outputMsg.appendChild(document.createElement('br'))
@ -887,8 +914,7 @@ function createTask(task) {
task['progressBar'] = taskEntry.querySelector('.progress-bar')
task['stopTask'] = taskEntry.querySelector('.stopTask')
task['stopTask'].addEventListener('click', async function(e) {
e.stopPropagation()
task['stopTask'].addEventListener('click', (e) => { shiftOrConfirm(e, "Are you sure? Should this task be stopped?", async function(e) {
if (task['isProcessing']) {
task.isProcessing = false
task.progressBar.classList.remove("active")
@ -905,7 +931,7 @@ function createTask(task) {
taskEntry.remove()
}
})
})})
task['useSettings'] = taskEntry.querySelector('.useSettings')
task['useSettings'].addEventListener('click', function(e) {
@ -934,8 +960,8 @@ function getPrompts() {
prompts = prompts.filter(prompt => prompt !== '')
if (activeTags.length > 0) {
const promptTags = activeTags.map(x => x.name).join(", ")
prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
const promptTags = activeTags.map(x => x.name).join(", ")
prompts = prompts.map((prompt) => `${prompt}, ${promptTags}`)
}
let promptsToMake = applySetOperator(prompts)
@ -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)

374
ui/media/js/parameters.js
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@ -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() {

20
ui/media/js/utils.js
View File

@ -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
View 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,

435
ui/sd_internal/runtime.py
View File

@ -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

45
ui/sd_internal/task_manager.py
View File

@ -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
View File

@ -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()