fix conflicts
This commit is contained in:
Greendayle
commit
2e8ba0fa47
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@ -16,7 +16,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- Attention, specify parts of text that the model should pay more attention to
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- a man in a ((tuxedo)) - will pay more attention to tuxedo
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- a man in a (tuxedo:1.21) - alternative syntax
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- select text and press ctrl+up or ctrl+down to aduotmatically adjust attention to selected text
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- select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
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- Loopback, run img2img processing multiple times
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- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
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- Textual Inversion
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@ -65,6 +65,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
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- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
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- separate prompts using uppercase `AND`
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- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
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- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
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## Installation and Running
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Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
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172
javascript/contextMenus.js
Normal file
172
javascript/contextMenus.js
Normal file
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@ -0,0 +1,172 @@
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contextMenuInit = function(){
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let eventListenerApplied=false;
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let menuSpecs = new Map();
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const uid = function(){
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return Date.now().toString(36) + Math.random().toString(36).substr(2);
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}
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function showContextMenu(event,element,menuEntries){
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let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
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let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
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let oldMenu = gradioApp().querySelector('#context-menu')
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if(oldMenu){
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oldMenu.remove()
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}
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let tabButton = gradioApp().querySelector('button')
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let baseStyle = window.getComputedStyle(tabButton)
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const contextMenu = document.createElement('nav')
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contextMenu.id = "context-menu"
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contextMenu.style.background = baseStyle.background
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contextMenu.style.color = baseStyle.color
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contextMenu.style.fontFamily = baseStyle.fontFamily
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contextMenu.style.top = posy+'px'
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contextMenu.style.left = posx+'px'
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const contextMenuList = document.createElement('ul')
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contextMenuList.className = 'context-menu-items';
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contextMenu.append(contextMenuList);
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menuEntries.forEach(function(entry){
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let contextMenuEntry = document.createElement('a')
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contextMenuEntry.innerHTML = entry['name']
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contextMenuEntry.addEventListener("click", function(e) {
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entry['func']();
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})
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contextMenuList.append(contextMenuEntry);
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})
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gradioApp().getRootNode().appendChild(contextMenu)
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let menuWidth = contextMenu.offsetWidth + 4;
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let menuHeight = contextMenu.offsetHeight + 4;
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let windowWidth = window.innerWidth;
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let windowHeight = window.innerHeight;
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if ( (windowWidth - posx) < menuWidth ) {
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contextMenu.style.left = windowWidth - menuWidth + "px";
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}
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if ( (windowHeight - posy) < menuHeight ) {
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contextMenu.style.top = windowHeight - menuHeight + "px";
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}
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}
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function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
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currentItems = menuSpecs.get(targetEmementSelector)
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if(!currentItems){
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currentItems = []
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menuSpecs.set(targetEmementSelector,currentItems);
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}
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let newItem = {'id':targetEmementSelector+'_'+uid(),
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'name':entryName,
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'func':entryFunction,
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'isNew':true}
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currentItems.push(newItem)
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return newItem['id']
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}
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function removeContextMenuOption(uid){
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menuSpecs.forEach(function(v,k) {
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let index = -1
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v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
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if(index>=0){
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v.splice(index, 1);
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}
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})
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}
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function addContextMenuEventListener(){
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if(eventListenerApplied){
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return;
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}
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gradioApp().addEventListener("click", function(e) {
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let source = e.composedPath()[0]
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if(source.id && source.indexOf('check_progress')>-1){
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return
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}
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let oldMenu = gradioApp().querySelector('#context-menu')
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if(oldMenu){
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oldMenu.remove()
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}
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});
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gradioApp().addEventListener("contextmenu", function(e) {
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let oldMenu = gradioApp().querySelector('#context-menu')
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if(oldMenu){
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oldMenu.remove()
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}
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menuSpecs.forEach(function(v,k) {
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if(e.composedPath()[0].matches(k)){
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showContextMenu(e,e.composedPath()[0],v)
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e.preventDefault()
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return
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}
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})
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});
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eventListenerApplied=true
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}
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return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
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}
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initResponse = contextMenuInit()
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appendContextMenuOption = initResponse[0]
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removeContextMenuOption = initResponse[1]
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addContextMenuEventListener = initResponse[2]
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//Start example Context Menu Items
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generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
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let genbutton = gradioApp().querySelector('#txt2img_generate');
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let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
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if(!interruptbutton.offsetParent){
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genbutton.click();
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}
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clearInterval(window.generateOnRepeatInterval)
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window.generateOnRepeatInterval = setInterval(function(){
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if(!interruptbutton.offsetParent){
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genbutton.click();
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}
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},
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500)}
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)
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cancelGenerateForever = function(){
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clearInterval(window.generateOnRepeatInterval)
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let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
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if(interruptbutton.offsetParent){
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interruptbutton.click();
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}
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}
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appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
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appendContextMenuOption('#roll','Roll three',
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function(){
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let rollbutton = gradioApp().querySelector('#roll');
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setTimeout(function(){rollbutton.click()},100)
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setTimeout(function(){rollbutton.click()},200)
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setTimeout(function(){rollbutton.click()},300)
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}
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)
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//End example Context Menu Items
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onUiUpdate(function(){
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addContextMenuEventListener()
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});
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@ -1,5 +1,5 @@
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addEventListener('keydown', (event) => {
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let target = event.originalTarget;
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let target = event.originalTarget || event.composedPath()[0];
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if (!target.hasAttribute("placeholder")) return;
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if (!target.placeholder.toLowerCase().includes("prompt")) return;
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@ -35,6 +35,7 @@ titles = {
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"Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
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"Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
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"Skip": "Stop processing current image and continue processing.",
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"Interrupt": "Stop processing images and return any results accumulated so far.",
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"Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
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@ -86,6 +86,9 @@ function showGalleryImage(){
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if(fullImg_preview != null){
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fullImg_preview.forEach(function function_name(e) {
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if (e.dataset.modded)
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return;
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e.dataset.modded = true;
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if(e && e.parentElement.tagName == 'DIV'){
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e.style.cursor='pointer'
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@ -1,8 +1,9 @@
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// code related to showing and updating progressbar shown as the image is being made
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global_progressbars = {}
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function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){
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function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
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var progressbar = gradioApp().getElementById(id_progressbar)
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var skip = id_skip ? gradioApp().getElementById(id_skip) : null
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var interrupt = gradioApp().getElementById(id_interrupt)
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if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
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@ -32,30 +33,37 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte
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var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
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if(!progressDiv){
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if (skip) {
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skip.style.display = "none"
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}
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interrupt.style.display = "none"
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}
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}
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window.setTimeout(function(){ requestMoreProgress(id_part, id_progressbar_span, id_interrupt) }, 500)
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window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
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});
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mutationObserver.observe( progressbar, { childList:true, subtree:true })
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}
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}
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onUiUpdate(function(){
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check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
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check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
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check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery')
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check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
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check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
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check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery')
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})
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function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){
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function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){
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btn = gradioApp().getElementById(id_part+"_check_progress");
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if(btn==null) return;
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btn.click();
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var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
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var skip = id_skip ? gradioApp().getElementById(id_skip) : null
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var interrupt = gradioApp().getElementById(id_interrupt)
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if(progressDiv && interrupt){
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if (skip) {
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skip.style.display = "block"
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}
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interrupt.style.display = "block"
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}
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}
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@ -4,6 +4,7 @@ import os
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import sys
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import importlib.util
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import shlex
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import platform
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dir_repos = "repositories"
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dir_tmp = "tmp"
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@ -31,6 +32,7 @@ def extract_arg(args, name):
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args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
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xformers = '--xformers' in args
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def repo_dir(name):
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@ -124,6 +126,12 @@ if not is_installed("gfpgan"):
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if not is_installed("clip"):
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run_pip(f"install {clip_package}", "clip")
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if not is_installed("xformers") and xformers:
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if platform.system() == "Windows":
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run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
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elif platform.system() == "Linux":
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run_pip("install xformers", "xformers")
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os.makedirs(dir_repos, exist_ok=True)
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git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
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@ -111,7 +111,7 @@ class UpscalerESRGAN(Upscaler):
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print("Unable to load %s from %s" % (self.model_path, filename))
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return None
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pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
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pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
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crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
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pretrained_net = fix_model_layers(crt_model, pretrained_net)
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@ -43,7 +43,7 @@ class Hypernetwork:
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def load_hypernetworks(path):
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res = {}
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for filename in glob.iglob(path + '**/*.pt', recursive=True):
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for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
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try:
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hn = Hypernetwork(filename)
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res[hn.name] = hn
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@ -32,6 +32,8 @@ def process_batch(p, input_dir, output_dir, args):
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for i, image in enumerate(images):
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state.job = f"{i+1} out of {len(images)}"
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if state.skipped:
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state.skipped = False
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if state.interrupted:
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break
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|
|
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@ -141,6 +141,7 @@ class Processed:
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self.all_subseeds = all_subseeds or [self.subseed]
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self.infotexts = infotexts or [info]
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def js(self):
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obj = {
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"prompt": self.prompt,
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@ -312,6 +313,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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os.makedirs(p.outpath_grids, exist_ok=True)
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modules.sd_hijack.model_hijack.apply_circular(p.tiling)
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modules.sd_hijack.model_hijack.clear_comments()
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comments = {}
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@ -349,6 +351,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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state.job_count = p.n_iter
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||||
for n in range(p.n_iter):
|
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if state.skipped:
|
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state.skipped = False
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|
||||
if state.interrupted:
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||||
break
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||||
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||||
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|
@ -375,7 +380,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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with devices.autocast():
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
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||||
|
||||
if state.interrupted:
|
||||
if state.interrupted or state.skipped:
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||||
|
||||
# if we are interruped, sample returns just noise
|
||||
# use the image collected previously in sampler loop
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||||
|
|
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|||
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|
@ -239,6 +239,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
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|||
|
||||
conds_list.append(conds_for_batch)
|
||||
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||||
# if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
|
||||
# and won't be able to torch.stack them. So this fixes that.
|
||||
token_count = max([x.shape[0] for x in tensors])
|
||||
for i in range(len(tensors)):
|
||||
if tensors[i].shape[0] != token_count:
|
||||
last_vector = tensors[i][-1:]
|
||||
last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
|
||||
tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
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||||
|
||||
return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
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||||
|
||||
|
||||
|
|
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@ -18,16 +18,17 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
|
|||
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
|
||||
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
|
||||
|
||||
|
||||
def apply_optimizations():
|
||||
undo_optimizations()
|
||||
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = silu
|
||||
|
||||
if cmd_opts.opt_split_attention_v1:
|
||||
if cmd_opts.xformers and shared.xformers_available and not torch.version.hip:
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
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elif cmd_opts.opt_split_attention_v1:
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.attention_CrossAttention_forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
||||
|
||||
|
||||
|
|
@ -37,6 +38,13 @@ def undo_optimizations():
|
|||
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
|
||||
|
||||
|
||||
def get_target_prompt_token_count(token_count):
|
||||
if token_count < 75:
|
||||
return 75
|
||||
|
||||
return math.ceil(token_count / 10) * 10
|
||||
|
||||
|
||||
class StableDiffusionModelHijack:
|
||||
fixes = None
|
||||
comments = []
|
||||
|
|
@ -82,10 +90,12 @@ class StableDiffusionModelHijack:
|
|||
for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
|
||||
layer.padding_mode = 'circular' if enable else 'zeros'
|
||||
|
||||
def clear_comments(self):
|
||||
self.comments = []
|
||||
|
||||
def tokenize(self, text):
|
||||
max_length = self.clip.max_length - 2
|
||||
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
|
||||
return remade_batch_tokens[0], token_count, max_length
|
||||
return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
|
||||
|
||||
|
||||
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
||||
|
|
@ -94,7 +104,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
self.wrapped = wrapped
|
||||
self.hijack: StableDiffusionModelHijack = hijack
|
||||
self.tokenizer = wrapped.tokenizer
|
||||
self.max_length = wrapped.max_length
|
||||
self.token_mults = {}
|
||||
|
||||
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
|
||||
|
|
@ -116,7 +125,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
def tokenize_line(self, line, used_custom_terms, hijack_comments):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
maxlen = self.wrapped.max_length
|
||||
|
||||
if opts.enable_emphasis:
|
||||
parsed = prompt_parser.parse_prompt_attention(line)
|
||||
|
|
@ -148,19 +156,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
used_custom_terms.append((embedding.name, embedding.checksum()))
|
||||
i += embedding_length_in_tokens
|
||||
|
||||
if len(remade_tokens) > maxlen - 2:
|
||||
vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
|
||||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
prompt_target_length = get_target_prompt_token_count(token_count)
|
||||
tokens_to_add = prompt_target_length - len(remade_tokens) + 1
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
|
||||
remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
|
||||
multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
|
||||
|
||||
return remade_tokens, fixes, multipliers, token_count
|
||||
|
||||
|
|
@ -177,7 +178,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if line in cache:
|
||||
remade_tokens, fixes, multipliers = cache[line]
|
||||
else:
|
||||
remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
token_count = max(current_token_count, token_count)
|
||||
|
||||
cache[line] = (remade_tokens, fixes, multipliers)
|
||||
|
||||
|
|
@ -191,7 +193,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
def process_text_old(self, text):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
maxlen = self.wrapped.max_length
|
||||
maxlen = self.wrapped.max_length # you get to stay at 77
|
||||
used_custom_terms = []
|
||||
remade_batch_tokens = []
|
||||
overflowing_words = []
|
||||
|
|
@ -263,17 +265,24 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
self.hijack.comments = hijack_comments
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
outputs = self.wrapped.transformer(input_ids=tokens)
|
||||
target_token_count = get_target_prompt_token_count(token_count) + 2
|
||||
|
||||
position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
|
||||
position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
|
||||
|
||||
remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
|
||||
tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
|
||||
z = outputs.last_hidden_state
|
||||
|
||||
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
||||
batch_multipliers = torch.asarray(batch_multipliers).to(device)
|
||||
batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
|
||||
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
|
||||
original_mean = z.mean()
|
||||
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
||||
new_mean = z.mean()
|
||||
|
|
|
|||
|
|
@ -1,4 +1,7 @@
|
|||
import math
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import torch
|
||||
from torch import einsum
|
||||
|
||||
|
|
@ -7,18 +10,37 @@ from einops import rearrange
|
|||
|
||||
from modules import shared
|
||||
|
||||
if shared.cmd_opts.xformers:
|
||||
try:
|
||||
import xformers.ops
|
||||
import functorch
|
||||
xformers._is_functorch_available = True
|
||||
shared.xformers_available = True
|
||||
except Exception:
|
||||
print("Cannot import xformers", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
|
||||
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
||||
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
v = self.to_v(context)
|
||||
|
||||
hypernetwork = shared.selected_hypernetwork()
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
del context, x
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
|
||||
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
|
||||
for i in range(0, q.shape[0], 2):
|
||||
|
|
@ -31,6 +53,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
|||
|
||||
r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
|
||||
del s2
|
||||
del q, k, v
|
||||
|
||||
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
|
||||
del r1
|
||||
|
|
@ -105,6 +128,25 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
return self.to_out(r2)
|
||||
|
||||
def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
hypernetwork = shared.selected_hypernetwork()
|
||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||
if hypernetwork_layers is not None:
|
||||
k_in = self.to_k(hypernetwork_layers[0](context))
|
||||
v_in = self.to_v(hypernetwork_layers[1](context))
|
||||
else:
|
||||
k_in = self.to_k(context)
|
||||
v_in = self.to_v(context)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
|
||||
|
||||
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
||||
def cross_attention_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
|
|
@ -167,3 +209,13 @@ def cross_attention_attnblock_forward(self, x):
|
|||
h3 += x
|
||||
|
||||
return h3
|
||||
|
||||
def xformers_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
q1 = self.q(h_).contiguous()
|
||||
k1 = self.k(h_).contiguous()
|
||||
v = self.v(h_).contiguous()
|
||||
out = xformers.ops.memory_efficient_attention(q1, k1, v)
|
||||
out = self.proj_out(out)
|
||||
return x+out
|
||||
|
|
|
|||
|
|
@ -122,7 +122,11 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
|
|||
pl_sd = torch.load(checkpoint_file, map_location="cpu")
|
||||
if "global_step" in pl_sd:
|
||||
print(f"Global Step: {pl_sd['global_step']}")
|
||||
sd = pl_sd["state_dict"]
|
||||
|
||||
if "state_dict" in pl_sd:
|
||||
sd = pl_sd["state_dict"]
|
||||
else:
|
||||
sd = pl_sd
|
||||
|
||||
model.load_state_dict(sd, strict=False)
|
||||
|
||||
|
|
|
|||
|
|
@ -106,7 +106,7 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs):
|
|||
seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted:
|
||||
if state.interrupted or state.skipped:
|
||||
break
|
||||
|
||||
yield x
|
||||
|
|
@ -142,6 +142,16 @@ class VanillaStableDiffusionSampler:
|
|||
assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
|
||||
cond = tensor
|
||||
|
||||
# for DDIM, shapes must match, we can't just process cond and uncond independently;
|
||||
# filling unconditional_conditioning with repeats of the last vector to match length is
|
||||
# not 100% correct but should work well enough
|
||||
if unconditional_conditioning.shape[1] < cond.shape[1]:
|
||||
last_vector = unconditional_conditioning[:, -1:]
|
||||
last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
|
||||
unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
|
||||
elif unconditional_conditioning.shape[1] > cond.shape[1]:
|
||||
unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
|
||||
|
||||
if self.mask is not None:
|
||||
img_orig = self.sampler.model.q_sample(self.init_latent, ts)
|
||||
x_dec = img_orig * self.mask + self.nmask * x_dec
|
||||
|
|
@ -221,18 +231,29 @@ class CFGDenoiser(torch.nn.Module):
|
|||
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
if tensor.shape[1] == uncond.shape[1]:
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
else:
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
|
||||
for batch_offset in range(0, tensor.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
b = min(a + batch_size, tensor.shape[0])
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
|
||||
|
||||
denoised_uncond = x_out[-batch_size:]
|
||||
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
|
||||
|
||||
denoised_uncond = x_out[-uncond.shape[0]:]
|
||||
denoised = torch.clone(denoised_uncond)
|
||||
|
||||
for i, conds in enumerate(conds_list):
|
||||
|
|
@ -254,7 +275,7 @@ def extended_trange(sampler, count, *args, **kwargs):
|
|||
seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
|
||||
|
||||
for x in seq:
|
||||
if state.interrupted:
|
||||
if state.interrupted or state.skipped:
|
||||
break
|
||||
|
||||
if sampler.stop_at is not None and x > sampler.stop_at:
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
|
|||
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
|
||||
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
|
||||
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
|
|
@ -73,7 +74,7 @@ device = devices.device
|
|||
|
||||
batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
|
||||
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
||||
|
||||
xformers_available = False
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
|
||||
|
|
@ -84,6 +85,7 @@ def selected_hypernetwork():
|
|||
|
||||
|
||||
class State:
|
||||
skipped = False
|
||||
interrupted = False
|
||||
job = ""
|
||||
job_no = 0
|
||||
|
|
@ -96,6 +98,9 @@ class State:
|
|||
current_image_sampling_step = 0
|
||||
textinfo = None
|
||||
|
||||
def skip(self):
|
||||
self.skipped = True
|
||||
|
||||
def interrupt(self):
|
||||
self.interrupted = True
|
||||
|
||||
|
|
@ -118,8 +123,6 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename)
|
|||
interrogator = modules.interrogate.InterrogateModels("interrogate")
|
||||
|
||||
face_restorers = []
|
||||
# This was moved to webui.py with the other model "setup" calls.
|
||||
# modules.sd_models.list_models()
|
||||
|
||||
|
||||
def realesrgan_models_names():
|
||||
|
|
|
|||
|
|
@ -192,6 +192,7 @@ def wrap_gradio_call(func, extra_outputs=None):
|
|||
# last item is always HTML
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
|
||||
|
||||
shared.state.skipped = False
|
||||
shared.state.interrupted = False
|
||||
shared.state.job_count = 0
|
||||
|
||||
|
|
@ -417,9 +418,16 @@ def create_toprow(is_img2img):
|
|||
|
||||
with gr.Column(scale=1):
|
||||
with gr.Row():
|
||||
skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
|
||||
interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
|
||||
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
|
||||
|
||||
skip.click(
|
||||
fn=lambda: shared.state.skip(),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
interrupt.click(
|
||||
fn=lambda: shared.state.interrupt(),
|
||||
inputs=[],
|
||||
|
|
@ -952,7 +960,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
custom_name = gr.Textbox(label="Custom Name (Optional)")
|
||||
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
|
||||
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
|
||||
save_as_half = gr.Checkbox(value=False, label="Safe as float16")
|
||||
save_as_half = gr.Checkbox(value=False, label="Save as float16")
|
||||
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
|
||||
|
||||
with gr.Column(variant='panel'):
|
||||
|
|
|
|||
|
|
@ -26,3 +26,4 @@ lark
|
|||
deepdanbooru
|
||||
tensorflow
|
||||
tensorflow-io
|
||||
functorch
|
||||
|
|
|
|||
|
|
@ -25,3 +25,4 @@ lark==1.1.2
|
|||
git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow]
|
||||
tensorflow==2.10.0
|
||||
tensorflow-io==0.27.0
|
||||
functorch==0.2.1
|
||||
|
|
|
|||
43
style.css
43
style.css
|
|
@ -398,10 +398,20 @@ input[type="range"]{
|
|||
|
||||
#txt2img_interrupt, #img2img_interrupt{
|
||||
position: absolute;
|
||||
width: 100%;
|
||||
width: 50%;
|
||||
height: 72px;
|
||||
background: #b4c0cc;
|
||||
border-radius: 8px;
|
||||
border-radius: 0px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
#txt2img_skip, #img2img_skip{
|
||||
position: absolute;
|
||||
width: 50%;
|
||||
right: 0px;
|
||||
height: 72px;
|
||||
background: #b4c0cc;
|
||||
border-radius: 0px;
|
||||
display: none;
|
||||
}
|
||||
|
||||
|
|
@ -415,4 +425,31 @@ input[type="range"]{
|
|||
|
||||
#img2img_image div.h-60{
|
||||
height: 480px;
|
||||
}
|
||||
}
|
||||
|
||||
#context-menu{
|
||||
z-index:9999;
|
||||
position:absolute;
|
||||
display:block;
|
||||
padding:0px 0;
|
||||
border:2px solid #a55000;
|
||||
border-radius:8px;
|
||||
box-shadow:1px 1px 2px #CE6400;
|
||||
width: 200px;
|
||||
}
|
||||
|
||||
.context-menu-items{
|
||||
list-style: none;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
.context-menu-items a{
|
||||
display:block;
|
||||
padding:5px;
|
||||
cursor:pointer;
|
||||
}
|
||||
|
||||
.context-menu-items a:hover{
|
||||
background: #a55000;
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user