diff options
| author | 不会画画的中医不是好程序员 <yfszzx@gmail.com> | 2022-10-10 20:21:25 +0800 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-10-10 20:21:25 +0800 |
| commit | 1e18a5ffcc439b72adaaf425c0b79f3acb34322e (patch) | |
| tree | 01f9c73c02076694a9bc3c965875646473771db8 | |
| parent | 23f2989799ee3911d2959cfceb74b921f20c9a51 (diff) | |
| parent | a3578233395e585e68c2118d3630cb2a961d4a36 (diff) | |
Merge branch 'AUTOMATIC1111:master' into master
42 files changed, 1226 insertions, 294 deletions
diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md new file mode 100644 index 00000000..86009613 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -0,0 +1,28 @@ +# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request! + +If you have a large change, pay special attention to this paragraph: + +> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature. + +Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on. + +**Describe what this pull request is trying to achieve.** + +A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code. + +**Additional notes and description of your changes** + +More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of. + +**Environment this was tested in** + +List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box. + - OS: [e.g. Windows, Linux] + - Browser [e.g. chrome, safari] + - Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB] + +**Screenshots or videos of your changes** + +If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made. + +This is **required** for anything that touches the user interface.
\ No newline at end of file @@ -16,7 +16,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Attention, specify parts of text that the model should pay more attention to
- a man in a ((tuxedo)) - will pay more attention to tuxedo
- a man in a (tuxedo:1.21) - alternative syntax
- - select text and press ctrl+up or ctrl+down to aduotmatically adjust attention to selected text
+ - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
- Textual Inversion
@@ -34,7 +34,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Sampling method selection
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
-- Correct seeds for batches
+- Correct seeds for batches
- Prompt length validation
- get length of prompt in tokens as you type
- get a warning after generation if some text was truncated
@@ -65,6 +65,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
+- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
+- DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
## Installation and Running
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.
@@ -122,4 +124,5 @@ The documentation was moved from this README over to the project's [wiki](https: - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
- (You)
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js new file mode 100644 index 00000000..2d82269f --- /dev/null +++ b/javascript/contextMenus.js @@ -0,0 +1,172 @@ +
+contextMenuInit = function(){
+ let eventListenerApplied=false;
+ let menuSpecs = new Map();
+
+ const uid = function(){
+ return Date.now().toString(36) + Math.random().toString(36).substr(2);
+ }
+
+ function showContextMenu(event,element,menuEntries){
+ let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
+ let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
+
+ let oldMenu = gradioApp().querySelector('#context-menu')
+ if(oldMenu){
+ oldMenu.remove()
+ }
+
+ let tabButton = gradioApp().querySelector('button')
+ let baseStyle = window.getComputedStyle(tabButton)
+
+ const contextMenu = document.createElement('nav')
+ contextMenu.id = "context-menu"
+ contextMenu.style.background = baseStyle.background
+ contextMenu.style.color = baseStyle.color
+ contextMenu.style.fontFamily = baseStyle.fontFamily
+ contextMenu.style.top = posy+'px'
+ contextMenu.style.left = posx+'px'
+
+
+
+ const contextMenuList = document.createElement('ul')
+ contextMenuList.className = 'context-menu-items';
+ contextMenu.append(contextMenuList);
+
+ menuEntries.forEach(function(entry){
+ let contextMenuEntry = document.createElement('a')
+ contextMenuEntry.innerHTML = entry['name']
+ contextMenuEntry.addEventListener("click", function(e) {
+ entry['func']();
+ })
+ contextMenuList.append(contextMenuEntry);
+
+ })
+
+ gradioApp().getRootNode().appendChild(contextMenu)
+
+ let menuWidth = contextMenu.offsetWidth + 4;
+ let menuHeight = contextMenu.offsetHeight + 4;
+
+ let windowWidth = window.innerWidth;
+ let windowHeight = window.innerHeight;
+
+ if ( (windowWidth - posx) < menuWidth ) {
+ contextMenu.style.left = windowWidth - menuWidth + "px";
+ }
+
+ if ( (windowHeight - posy) < menuHeight ) {
+ contextMenu.style.top = windowHeight - menuHeight + "px";
+ }
+
+ }
+
+ function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
+
+ currentItems = menuSpecs.get(targetEmementSelector)
+
+ if(!currentItems){
+ currentItems = []
+ menuSpecs.set(targetEmementSelector,currentItems);
+ }
+ let newItem = {'id':targetEmementSelector+'_'+uid(),
+ 'name':entryName,
+ 'func':entryFunction,
+ 'isNew':true}
+
+ currentItems.push(newItem)
+ return newItem['id']
+ }
+
+ function removeContextMenuOption(uid){
+ menuSpecs.forEach(function(v,k) {
+ let index = -1
+ v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
+ if(index>=0){
+ v.splice(index, 1);
+ }
+ })
+ }
+
+ function addContextMenuEventListener(){
+ if(eventListenerApplied){
+ return;
+ }
+ gradioApp().addEventListener("click", function(e) {
+ let source = e.composedPath()[0]
+ if(source.id && source.indexOf('check_progress')>-1){
+ return
+ }
+
+ let oldMenu = gradioApp().querySelector('#context-menu')
+ if(oldMenu){
+ oldMenu.remove()
+ }
+ });
+ gradioApp().addEventListener("contextmenu", function(e) {
+ let oldMenu = gradioApp().querySelector('#context-menu')
+ if(oldMenu){
+ oldMenu.remove()
+ }
+ menuSpecs.forEach(function(v,k) {
+ if(e.composedPath()[0].matches(k)){
+ showContextMenu(e,e.composedPath()[0],v)
+ e.preventDefault()
+ return
+ }
+ })
+ });
+ eventListenerApplied=true
+
+ }
+
+ return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
+}
+
+initResponse = contextMenuInit()
+appendContextMenuOption = initResponse[0]
+removeContextMenuOption = initResponse[1]
+addContextMenuEventListener = initResponse[2]
+
+
+//Start example Context Menu Items
+generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+ let genbutton = gradioApp().querySelector('#txt2img_generate');
+ let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+ if(!interruptbutton.offsetParent){
+ genbutton.click();
+ }
+ clearInterval(window.generateOnRepeatInterval)
+ window.generateOnRepeatInterval = setInterval(function(){
+ if(!interruptbutton.offsetParent){
+ genbutton.click();
+ }
+ },
+ 500)}
+)
+
+cancelGenerateForever = function(){
+ clearInterval(window.generateOnRepeatInterval)
+ let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+ if(interruptbutton.offsetParent){
+ interruptbutton.click();
+ }
+}
+
+appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
+appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
+
+
+appendContextMenuOption('#roll','Roll three',
+ function(){
+ let rollbutton = gradioApp().querySelector('#roll');
+ setTimeout(function(){rollbutton.click()},100)
+ setTimeout(function(){rollbutton.click()},200)
+ setTimeout(function(){rollbutton.click()},300)
+ }
+)
+//End example Context Menu Items
+
+onUiUpdate(function(){
+ addContextMenuEventListener()
+});
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index c67ed579..0280c603 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,5 +1,5 @@ addEventListener('keydown', (event) => {
- let target = event.originalTarget;
+ let target = event.originalTarget || event.composedPath()[0];
if (!target.hasAttribute("placeholder")) return;
if (!target.placeholder.toLowerCase().includes("prompt")) return;
diff --git a/javascript/hints.js b/javascript/hints.js index 8adcd983..8e352e94 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -35,6 +35,7 @@ titles = { "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.", "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.", + "Skip": "Stop processing current image and continue processing.", "Interrupt": "Stop processing images and return any results accumulated so far.", "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index 3a0baac8..65a33dd7 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -1,72 +1,97 @@ // A full size 'lightbox' preview modal shown when left clicking on gallery previews - function closeModal() { - gradioApp().getElementById("lightboxModal").style.display = "none"; + gradioApp().getElementById("lightboxModal").style.display = "none"; } function showModal(event) { - const source = event.target || event.srcElement; - const modalImage = gradioApp().getElementById("modalImage") - const lb = gradioApp().getElementById("lightboxModal") - modalImage.src = source.src - if (modalImage.style.display === 'none') { - lb.style.setProperty('background-image', 'url(' + source.src + ')'); - } - lb.style.display = "block"; - lb.focus() - event.stopPropagation() + const source = event.target || event.srcElement; + const modalImage = gradioApp().getElementById("modalImage") + const lb = gradioApp().getElementById("lightboxModal") + modalImage.src = source.src + if (modalImage.style.display === 'none') { + lb.style.setProperty('background-image', 'url(' + source.src + ')'); + } + lb.style.display = "block"; + lb.focus() + event.stopPropagation() } function negmod(n, m) { - return ((n % m) + m) % m; + return ((n % m) + m) % m; } -function modalImageSwitch(offset){ - var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all") - var galleryButtons = [] - allgalleryButtons.forEach(function(elem){ - if(elem.parentElement.offsetParent){ - galleryButtons.push(elem); +function updateOnBackgroundChange() { + const modalImage = gradioApp().getElementById("modalImage") + if (modalImage && modalImage.offsetParent) { + let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") + let currentButton = null + allcurrentButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + currentButton = elem; + } + }) + + if (modalImage.src != currentButton.children[0].src) { + modalImage.src = currentButton.children[0].src; + if (modalImage.style.display === 'none') { + modal.style.setProperty('background-image', `url(${modalImage.src})`) + } + } } - }) - - if(galleryButtons.length>1){ - var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - var currentButton = null - allcurrentButtons.forEach(function(elem){ - if(elem.parentElement.offsetParent){ - currentButton = elem; +} + +function modalImageSwitch(offset) { + var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all") + var galleryButtons = [] + allgalleryButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + galleryButtons.push(elem); } - }) - - var result = -1 - galleryButtons.forEach(function(v, i){ if(v==currentButton) { result = i } }) - - if(result != -1){ - nextButton = galleryButtons[negmod((result+offset),galleryButtons.length)] - nextButton.click() - const modalImage = gradioApp().getElementById("modalImage"); - const modal = gradioApp().getElementById("lightboxModal"); - modalImage.src = nextButton.children[0].src; - if (modalImage.style.display === 'none') { - modal.style.setProperty('background-image', `url(${modalImage.src})`) + }) + + if (galleryButtons.length > 1) { + var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") + var currentButton = null + allcurrentButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + currentButton = elem; + } + }) + + var result = -1 + galleryButtons.forEach(function(v, i) { + if (v == currentButton) { + result = i + } + }) + + if (result != -1) { + nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)] + nextButton.click() + const modalImage = gradioApp().getElementById("modalImage"); + const modal = gradioApp().getElementById("lightboxModal"); + modalImage.src = nextButton.children[0].src; + if (modalImage.style.display === 'none') { + modal.style.setProperty('background-image', `url(${modalImage.src})`) + } + setTimeout(function() { + modal.focus() + }, 10) } - setTimeout( function(){modal.focus()},10) - } - } + } } -function modalNextImage(event){ - modalImageSwitch(1) - event.stopPropagation() +function modalNextImage(event) { + modalImageSwitch(1) + event.stopPropagation() } -function modalPrevImage(event){ - modalImageSwitch(-1) - event.stopPropagation() +function modalPrevImage(event) { + modalImageSwitch(-1) + event.stopPropagation() } -function modalKeyHandler(event){ +function modalKeyHandler(event) { switch (event.key) { case "ArrowLeft": modalPrevImage(event) @@ -80,21 +105,22 @@ function modalKeyHandler(event){ } } -function showGalleryImage(){ +function showGalleryImage() { setTimeout(function() { fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain') - - if(fullImg_preview != null){ + + if (fullImg_preview != null) { fullImg_preview.forEach(function function_name(e) { + if (e.dataset.modded) + return; + e.dataset.modded = true; if(e && e.parentElement.tagName == 'DIV'){ - e.style.cursor='pointer' - e.addEventListener('click', function (evt) { if(!opts.js_modal_lightbox) return; - modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initialy_zoomed) + modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) showModal(evt) - },true); + }, true); } }); } @@ -102,21 +128,21 @@ function showGalleryImage(){ }, 100); } -function modalZoomSet(modalImage, enable){ - if( enable ){ +function modalZoomSet(modalImage, enable) { + if (enable) { modalImage.classList.add('modalImageFullscreen'); - } else{ + } else { modalImage.classList.remove('modalImageFullscreen'); } } -function modalZoomToggle(event){ +function modalZoomToggle(event) { modalImage = gradioApp().getElementById("modalImage"); modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')) event.stopPropagation() } -function modalTileImageToggle(event){ +function modalTileImageToggle(event) { const modalImage = gradioApp().getElementById("modalImage"); const modal = gradioApp().getElementById("lightboxModal"); const isTiling = modalImage.style.display === 'none'; @@ -131,17 +157,18 @@ function modalTileImageToggle(event){ event.stopPropagation() } -function galleryImageHandler(e){ - if(e && e.parentElement.tagName == 'BUTTON'){ +function galleryImageHandler(e) { + if (e && e.parentElement.tagName == 'BUTTON') { e.onclick = showGalleryImage; } } -onUiUpdate(function(){ +onUiUpdate(function() { fullImg_preview = gradioApp().querySelectorAll('img.w-full') - if(fullImg_preview != null){ - fullImg_preview.forEach(galleryImageHandler); + if (fullImg_preview != null) { + fullImg_preview.forEach(galleryImageHandler); } + updateOnBackgroundChange(); }) document.addEventListener("DOMContentLoaded", function() { @@ -149,13 +176,13 @@ document.addEventListener("DOMContentLoaded", function() { const modal = document.createElement('div') modal.onclick = closeModal; modal.id = "lightboxModal"; - modal.tabIndex=0 + modal.tabIndex = 0 modal.addEventListener('keydown', modalKeyHandler, true) const modalControls = document.createElement('div') modalControls.className = 'modalControls gradio-container'; modal.append(modalControls); - + const modalZoom = document.createElement('span') modalZoom.className = 'modalZoom cursor'; modalZoom.innerHTML = '⤡' @@ -180,30 +207,30 @@ document.addEventListener("DOMContentLoaded", function() { const modalImage = document.createElement('img') modalImage.id = 'modalImage'; modalImage.onclick = closeModal; - modalImage.tabIndex=0 + modalImage.tabIndex = 0 modalImage.addEventListener('keydown', modalKeyHandler, true) modal.appendChild(modalImage) const modalPrev = document.createElement('a') modalPrev.className = 'modalPrev'; modalPrev.innerHTML = '❮' - modalPrev.tabIndex=0 - modalPrev.addEventListener('click',modalPrevImage,true); + modalPrev.tabIndex = 0 + modalPrev.addEventListener('click', modalPrevImage, true); modalPrev.addEventListener('keydown', modalKeyHandler, true) modal.appendChild(modalPrev) const modalNext = document.createElement('a') modalNext.className = 'modalNext'; modalNext.innerHTML = '❯' - modalNext.tabIndex=0 - modalNext.addEventListener('click',modalNextImage,true); + modalNext.tabIndex = 0 + modalNext.addEventListener('click', modalNextImage, true); modalNext.addEventListener('keydown', modalKeyHandler, true) modal.appendChild(modalNext) gradioApp().getRootNode().appendChild(modal) - + document.body.appendChild(modalFragment); - + }); diff --git a/javascript/progressbar.js b/javascript/progressbar.js index f9e9290e..4395a215 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -1,8 +1,9 @@ // code related to showing and updating progressbar shown as the image is being made global_progressbars = {} -function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){ +function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ var progressbar = gradioApp().getElementById(id_progressbar) + var skip = id_skip ? gradioApp().getElementById(id_skip) : null var interrupt = gradioApp().getElementById(id_interrupt) if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){ @@ -32,30 +33,37 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; if(!progressDiv){ + if (skip) { + skip.style.display = "none" + } interrupt.style.display = "none" } } - window.setTimeout(function(){ requestMoreProgress(id_part, id_progressbar_span, id_interrupt) }, 500) + window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500) }); mutationObserver.observe( progressbar, { childList:true, subtree:true }) } } onUiUpdate(function(){ - check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') - check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') - check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery') + check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') + check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') + check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery') }) -function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){ +function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){ btn = gradioApp().getElementById(id_part+"_check_progress"); if(btn==null) return; btn.click(); var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; + var skip = id_skip ? gradioApp().getElementById(id_skip) : null var interrupt = gradioApp().getElementById(id_interrupt) if(progressDiv && interrupt){ + if (skip) { + skip.style.display = "block" + } interrupt.style.display = "block" } } @@ -4,39 +4,17 @@ import os import sys
import importlib.util
import shlex
+import platform
dir_repos = "repositories"
-dir_tmp = "tmp"
-
python = sys.executable
git = os.environ.get('GIT', "git")
-torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
-requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
-
-gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
-clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-
-stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
-taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
-k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
-codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
-blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
-
-args = shlex.split(commandline_args)
def extract_arg(args, name):
return [x for x in args if x != name], name in args
-args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
-
-
-def repo_dir(name):
- return os.path.join(dir_repos, name)
-
-
def run(command, desc=None, errdesc=None):
if desc is not None:
print(desc)
@@ -56,23 +34,11 @@ stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.st return result.stdout.decode(encoding="utf8", errors="ignore")
-def run_python(code, desc=None, errdesc=None):
- return run(f'"{python}" -c "{code}"', desc, errdesc)
-
-
-def run_pip(args, desc=None):
- return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
-
-
def check_run(command):
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
return result.returncode == 0
-def check_run_python(code):
- return check_run(f'"{python}" -c "{code}"')
-
-
def is_installed(package):
try:
spec = importlib.util.find_spec(package)
@@ -82,6 +48,22 @@ def is_installed(package): return spec is not None
+def repo_dir(name):
+ return os.path.join(dir_repos, name)
+
+
+def run_python(code, desc=None, errdesc=None):
+ return run(f'"{python}" -c "{code}"', desc, errdesc)
+
+
+def run_pip(args, desc=None):
+ return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+
+
+def check_run_python(code):
+ return check_run(f'"{python}" -c "{code}"')
+
+
def git_clone(url, dir, name, commithash=None):
# TODO clone into temporary dir and move if successful
@@ -103,50 +85,81 @@ def git_clone(url, dir, name, commithash=None): run(f'"{git}" -C {dir} checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
-try:
- commit = run(f"{git} rev-parse HEAD").strip()
-except Exception:
- commit = "<none>"
+def prepare_enviroment():
+ torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
+ requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+ commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+
+ gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
+ clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-print(f"Python {sys.version}")
-print(f"Commit hash: {commit}")
+ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
+ taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
+ k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
+ codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
+ blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
+ args = shlex.split(commandline_args)
+
+ args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
+ xformers = '--xformers' in args
+ deepdanbooru = '--deepdanbooru' in args
+
+ try:
+ commit = run(f"{git} rev-parse HEAD").strip()
+ except Exception:
+ commit = "<none>"
-if not is_installed("torch") or not is_installed("torchvision"):
- run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
+ print(f"Python {sys.version}")
+ print(f"Commit hash: {commit}")
-if not skip_torch_cuda_test:
- run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
+ if not is_installed("torch") or not is_installed("torchvision"):
+ run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
-if not is_installed("gfpgan"):
- run_pip(f"install {gfpgan_package}", "gfpgan")
+ if not skip_torch_cuda_test:
+ run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
-if not is_installed("clip"):
- run_pip(f"install {clip_package}", "clip")
+ if not is_installed("gfpgan"):
+ run_pip(f"install {gfpgan_package}", "gfpgan")
-os.makedirs(dir_repos, exist_ok=True)
+ if not is_installed("clip"):
+ run_pip(f"install {clip_package}", "clip")
-git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+ if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
+ if platform.system() == "Windows":
+ run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+ elif platform.system() == "Linux":
+ run_pip("install xformers", "xformers")
-if not is_installed("lpips"):
- run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+ if not is_installed("deepdanbooru") and deepdanbooru:
+ run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
-run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+ os.makedirs(dir_repos, exist_ok=True)
-sys.argv += args
+ git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+ git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+ git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+ git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+ git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+
+ if not is_installed("lpips"):
+ run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+
+ run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+
+ sys.argv += args
+
+ if "--exit" in args:
+ print("Exiting because of --exit argument")
+ exit(0)
-if "--exit" in args:
- print("Exiting because of --exit argument")
- exit(0)
def start_webui():
print(f"Launching Web UI with arguments: {' '.join(sys.argv[1:])}")
import webui
webui.webui()
+
if __name__ == "__main__":
+ prepare_enviroment()
start_webui()
diff --git a/models/deepbooru/Put your deepbooru release project folder here.txt b/models/deepbooru/Put your deepbooru release project folder here.txt new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/models/deepbooru/Put your deepbooru release project folder here.txt diff --git a/modules/bsrgan_model.py b/modules/bsrgan_model.py index 3bd80791..737e1a76 100644 --- a/modules/bsrgan_model.py +++ b/modules/bsrgan_model.py @@ -10,13 +10,11 @@ from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from modules.bsrgan_model_arch import RRDBNet -from modules.paths import models_path class UpscalerBSRGAN(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "BSRGAN" - self.model_path = os.path.join(models_path, self.name) self.model_name = "BSRGAN 4x" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/BSRGAN.pth" self.user_path = dirname diff --git a/modules/deepbooru.py b/modules/deepbooru.py new file mode 100644 index 00000000..7e3c0618 --- /dev/null +++ b/modules/deepbooru.py @@ -0,0 +1,73 @@ +import os.path +from concurrent.futures import ProcessPoolExecutor +from multiprocessing import get_context + + +def _load_tf_and_return_tags(pil_image, threshold): + import deepdanbooru as dd + import tensorflow as tf + import numpy as np + + this_folder = os.path.dirname(__file__) + model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru')) + if not os.path.exists(os.path.join(model_path, 'project.json')): + # there is no point importing these every time + import zipfile + from basicsr.utils.download_util import load_file_from_url + load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip", + model_path) + with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref: + zip_ref.extractall(model_path) + os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip")) + + tags = dd.project.load_tags_from_project(model_path) + model = dd.project.load_model_from_project( + model_path, compile_model=True + ) + + width = model.input_shape[2] + height = model.input_shape[1] + image = np.array(pil_image) + image = tf.image.resize( + image, + size=(height, width), + method=tf.image.ResizeMethod.AREA, + preserve_aspect_ratio=True, + ) + image = image.numpy() # EagerTensor to np.array + image = dd.image.transform_and_pad_image(image, width, height) + image = image / 255.0 + image_shape = image.shape + image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2])) + + y = model.predict(image)[0] + + result_dict = {} + + for i, tag in enumerate(tags): + result_dict[tag] = y[i] + result_tags_out = [] + result_tags_print = [] + for tag in tags: + if result_dict[tag] >= threshold: + if tag.startswith("rating:"): + continue + result_tags_out.append(tag) + result_tags_print.append(f'{result_dict[tag]} {tag}') + + print('\n'.join(sorted(result_tags_print, reverse=True))) + + return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ') + + +def subprocess_init_no_cuda(): + import os + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + + +def get_deepbooru_tags(pil_image, threshold=0.5): + context = get_context('spawn') + with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor: + f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, ) + ret = f.result() # will rethrow any exceptions + return ret
\ No newline at end of file diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index d17e730f..46ad0da3 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -5,9 +5,8 @@ import torch from PIL import Image
from basicsr.utils.download_util import load_file_from_url
-import modules.esrgam_model_arch as arch
+import modules.esrgan_model_arch as arch
from modules import shared, modelloader, images, devices
-from modules.paths import models_path
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
@@ -76,7 +75,6 @@ class UpscalerESRGAN(Upscaler): self.model_name = "ESRGAN_4x"
self.scalers = []
self.user_path = dirname
- self.model_path = os.path.join(models_path, self.name)
super().__init__()
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
scalers = []
@@ -111,7 +109,7 @@ class UpscalerESRGAN(Upscaler): print("Unable to load %s from %s" % (self.model_path, filename))
return None
- pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
+ pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
pretrained_net = fix_model_layers(crt_model, pretrained_net)
diff --git a/modules/esrgam_model_arch.py b/modules/esrgan_model_arch.py index e413d36e..e413d36e 100644 --- a/modules/esrgam_model_arch.py +++ b/modules/esrgan_model_arch.py diff --git a/modules/extras.py b/modules/extras.py index 1d9e64e5..41e8612c 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -29,7 +29,7 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v if extras_mode == 1:
#convert file to pillow image
for img in image_folder:
- image = Image.fromarray(np.array(Image.open(img)))
+ image = Image.open(img)
imageArr.append(image)
imageNameArr.append(os.path.splitext(img.orig_name)[0])
else:
@@ -98,6 +98,10 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo,
forced_filename=image_name if opts.use_original_name_batch else None)
+ if opts.enable_pnginfo:
+ image.info = existing_pnginfo
+ image.info["extras"] = info
+
outputs.append(image)
devices.torch_gc()
@@ -169,9 +173,9 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int print(f"Loading {secondary_model_info.filename}...")
secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-
- theta_0 = primary_model['state_dict']
- theta_1 = secondary_model['state_dict']
+
+ theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
+ theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
theta_funcs = {
"Weighted Sum": weighted_sum,
diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py new file mode 100644 index 00000000..498bc9d8 --- /dev/null +++ b/modules/hypernetwork.py @@ -0,0 +1,98 @@ +import glob
+import os
+import sys
+import traceback
+
+import torch
+
+from ldm.util import default
+from modules import devices, shared
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+
+
+class HypernetworkModule(torch.nn.Module):
+ def __init__(self, dim, state_dict):
+ super().__init__()
+
+ self.linear1 = torch.nn.Linear(dim, dim * 2)
+ self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+ self.load_state_dict(state_dict, strict=True)
+ self.to(devices.device)
+
+ def forward(self, x):
+ return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+ filename = None
+ name = None
+
+ def __init__(self, filename):
+ self.filename = filename
+ self.name = os.path.splitext(os.path.basename(filename))[0]
+ self.layers = {}
+
+ state_dict = torch.load(filename, map_location='cpu')
+ for size, sd in state_dict.items():
+ self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+
+def list_hypernetworks(path):
+ res = {}
+ for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+ name = os.path.splitext(os.path.basename(filename))[0]
+ res[name] = filename
+ return res
+
+
+def load_hypernetwork(filename):
+ path = shared.hypernetworks.get(filename, None)
+ if path is not None:
+ print(f"Loading hypernetwork {filename}")
+ try:
+ shared.loaded_hypernetwork = Hypernetwork(path)
+ except Exception:
+ print(f"Error loading hypernetwork {path}", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ else:
+ if shared.loaded_hypernetwork is not None:
+ print(f"Unloading hypernetwork")
+
+ shared.loaded_hypernetwork = None
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+
+ hypernetwork = shared.loaded_hypernetwork
+ hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+ if hypernetwork_layers is not None:
+ k = self.to_k(hypernetwork_layers[0](context))
+ v = self.to_v(hypernetwork_layers[1](context))
+ else:
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+ if mask is not None:
+ mask = rearrange(mask, 'b ... -> b (...)')
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ sim.masked_fill_(~mask, max_neg_value)
+
+ # attention, what we cannot get enough of
+ attn = sim.softmax(dim=-1)
+
+ out = einsum('b i j, b j d -> b i d', attn, v)
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(out)
diff --git a/modules/images.py b/modules/images.py index 29c5ee24..c0a90676 100644 --- a/modules/images.py +++ b/modules/images.py @@ -349,6 +349,38 @@ def get_next_sequence_number(path, basename): def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
+ '''Save an image.
+
+ Args:
+ image (`PIL.Image`):
+ The image to be saved.
+ path (`str`):
+ The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
+ basename (`str`):
+ The base filename which will be applied to `filename pattern`.
+ seed, prompt, short_filename,
+ extension (`str`):
+ Image file extension, default is `png`.
+ pngsectionname (`str`):
+ Specify the name of the section which `info` will be saved in.
+ info (`str` or `PngImagePlugin.iTXt`):
+ PNG info chunks.
+ existing_info (`dict`):
+ Additional PNG info. `existing_info == {pngsectionname: info, ...}`
+ no_prompt:
+ TODO I don't know its meaning.
+ p (`StableDiffusionProcessing`)
+ forced_filename (`str`):
+ If specified, `basename` and filename pattern will be ignored.
+ save_to_dirs (bool):
+ If true, the image will be saved into a subdirectory of `path`.
+
+ Returns: (fullfn, txt_fullfn)
+ fullfn (`str`):
+ The full path of the saved imaged.
+ txt_fullfn (`str` or None):
+ If a text file is saved for this image, this will be its full path. Otherwise None.
+ '''
if short_filename or prompt is None or seed is None:
file_decoration = ""
elif opts.save_to_dirs:
@@ -424,7 +456,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i piexif.insert(exif_bytes(), fullfn_without_extension + ".jpg")
if opts.save_txt and info is not None:
- with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
+ txt_fullfn = f"{fullfn_without_extension}.txt"
+ with open(txt_fullfn, "w", encoding="utf8") as file:
file.write(info + "\n")
+ else:
+ txt_fullfn = None
- return fullfn
+ return fullfn, txt_fullfn
diff --git a/modules/img2img.py b/modules/img2img.py index da212d72..24126774 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -32,6 +32,8 @@ def process_batch(p, input_dir, output_dir, args): for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
+ if state.skipped:
+ state.skipped = False
if state.interrupted:
break
diff --git a/modules/interrogate.py b/modules/interrogate.py index eed87144..635e266e 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -140,11 +140,11 @@ class InterrogateModels: res = caption
- cilp_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+ clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
with torch.no_grad(), precision_scope("cuda"):
- image_features = self.clip_model.encode_image(cilp_image).type(self.dtype)
+ image_features = self.clip_model.encode_image(clip_image).type(self.dtype)
image_features /= image_features.norm(dim=-1, keepdim=True)
diff --git a/modules/ldsr_model.py b/modules/ldsr_model.py index 1c1070fc..8c4db44a 100644 --- a/modules/ldsr_model.py +++ b/modules/ldsr_model.py @@ -7,13 +7,11 @@ from basicsr.utils.download_util import load_file_from_url from modules.upscaler import Upscaler, UpscalerData from modules.ldsr_model_arch import LDSR from modules import shared -from modules.paths import models_path class UpscalerLDSR(Upscaler): def __init__(self, user_path): self.name = "LDSR" - self.model_path = os.path.join(models_path, self.name) self.user_path = user_path self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1" self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1" diff --git a/modules/paths.py b/modules/paths.py index 606f7d66..1e7a2fbc 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -1,6 +1,7 @@ import argparse
import os
import sys
+import modules.safe
script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
models_path = os.path.join(script_path, "models")
@@ -12,6 +13,7 @@ possible_sd_paths = [os.path.join(script_path, 'repositories/stable-diffusion'), for possible_sd_path in possible_sd_paths:
if os.path.exists(os.path.join(possible_sd_path, 'ldm/models/diffusion/ddpm.py')):
sd_path = os.path.abspath(possible_sd_path)
+ break
assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
diff --git a/modules/processing.py b/modules/processing.py index f773a30e..94d2dd62 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -46,6 +46,12 @@ def apply_color_correction(correction, image): return image
+def get_correct_sampler(p):
+ if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
+ return sd_samplers.samplers
+ elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
+ return sd_samplers.samplers_for_img2img
+
class StableDiffusionProcessing:
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
self.sd_model = sd_model
@@ -123,6 +129,7 @@ class Processed: self.index_of_first_image = index_of_first_image
self.styles = p.styles
self.job_timestamp = state.job_timestamp
+ self.clip_skip = opts.CLIP_stop_at_last_layers
self.eta = p.eta
self.ddim_discretize = p.ddim_discretize
@@ -169,6 +176,7 @@ class Processed: "infotexts": self.infotexts,
"styles": self.styles,
"job_timestamp": self.job_timestamp,
+ "clip_skip": self.clip_skip,
}
return json.dumps(obj)
@@ -266,14 +274,18 @@ def fix_seed(p): def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
index = position_in_batch + iteration * p.batch_size
+ clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
+
generation_params = {
"Steps": p.steps,
- "Sampler": sd_samplers.samplers[p.sampler_index].name,
+ "Sampler": get_correct_sampler(p)[p.sampler_index].name,
"CFG scale": p.cfg_scale,
"Seed": all_seeds[index],
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
"Size": f"{p.width}x{p.height}",
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
+ "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+ "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
"Batch size": (None if p.batch_size < 2 else p.batch_size),
"Batch pos": (None if p.batch_size < 2 else position_in_batch),
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
@@ -281,6 +293,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
"Denoising strength": getattr(p, 'denoising_strength', None),
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
+ "Clip skip": None if clip_skip <= 1 else clip_skip,
}
generation_params.update(p.extra_generation_params)
@@ -312,6 +325,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: os.makedirs(p.outpath_grids, exist_ok=True)
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+ modules.sd_hijack.model_hijack.clear_comments()
comments = {}
@@ -341,7 +355,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: infotexts = []
output_images = []
- with torch.no_grad():
+ with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast():
p.init(all_prompts, all_seeds, all_subseeds)
@@ -349,6 +363,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: state.job_count = p.n_iter
for n in range(p.n_iter):
+ if state.skipped:
+ state.skipped = False
+
if state.interrupted:
break
@@ -375,9 +392,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
- if state.interrupted:
+ if state.interrupted or state.skipped:
- # if we are interruped, sample returns just noise
+ # if we are interrupted, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
@@ -436,7 +453,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: text = infotext(n, i)
infotexts.append(text)
- image.info["parameters"] = text
+ if opts.enable_pnginfo:
+ image.info["parameters"] = text
output_images.append(image)
del x_samples_ddim
@@ -455,7 +473,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if opts.return_grid:
text = infotext()
infotexts.insert(0, text)
- grid.info["parameters"] = text
+ if opts.enable_pnginfo:
+ grid.info["parameters"] = text
output_images.insert(0, grid)
index_of_first_image = 1
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index f00256f2..919d5d31 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -13,13 +13,14 @@ import lark schedule_parser = lark.Lark(r"""
!start: (prompt | /[][():]/+)*
-prompt: (emphasized | scheduled | plain | WHITESPACE)*
+prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
!emphasized: "(" prompt ")"
| "(" prompt ":" prompt ")"
| "[" prompt "]"
scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
+alternate: "[" prompt ("|" prompt)+ "]"
WHITESPACE: /\s+/
-plain: /([^\\\[\]():]|\\.)+/
+plain: /([^\\\[\]():|]|\\.)+/
%import common.SIGNED_NUMBER -> NUMBER
""")
@@ -59,6 +60,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): tree.children[-1] *= steps
tree.children[-1] = min(steps, int(tree.children[-1]))
l.append(tree.children[-1])
+ def alternate(self, tree):
+ l.extend(range(1, steps+1))
CollectSteps().visit(tree)
return sorted(set(l))
@@ -67,6 +70,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps): def scheduled(self, args):
before, after, _, when = args
yield before or () if step <= when else after
+ def alternate(self, args):
+ yield next(args[(step - 1)%len(args)])
def start(self, args):
def flatten(x):
if type(x) == str:
@@ -239,6 +244,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): conds_list.append(conds_for_batch)
+ # 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])
+
return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index dc0123e0..3ac0b97a 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -8,14 +8,12 @@ from basicsr.utils.download_util import load_file_from_url from realesrgan import RealESRGANer
from modules.upscaler import Upscaler, UpscalerData
-from modules.paths import models_path
from modules.shared import cmd_opts, opts
class UpscalerRealESRGAN(Upscaler):
def __init__(self, path):
self.name = "RealESRGAN"
- self.model_path = os.path.join(models_path, self.name)
self.user_path = path
super().__init__()
try:
diff --git a/modules/safe.py b/modules/safe.py new file mode 100644 index 00000000..05917463 --- /dev/null +++ b/modules/safe.py @@ -0,0 +1,93 @@ +# this code is adapted from the script contributed by anon from /h/
+
+import io
+import pickle
+import collections
+import sys
+import traceback
+
+import torch
+import numpy
+import _codecs
+import zipfile
+
+
+# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
+TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
+
+
+def encode(*args):
+ out = _codecs.encode(*args)
+ return out
+
+
+class RestrictedUnpickler(pickle.Unpickler):
+ def persistent_load(self, saved_id):
+ assert saved_id[0] == 'storage'
+ return TypedStorage()
+
+ def find_class(self, module, name):
+ if module == 'collections' and name == 'OrderedDict':
+ return getattr(collections, name)
+ if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
+ return getattr(torch._utils, name)
+ if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
+ return getattr(torch, name)
+ if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
+ return getattr(torch.nn.modules.container, name)
+ if module == 'numpy.core.multiarray' and name == 'scalar':
+ return numpy.core.multiarray.scalar
+ if module == 'numpy' and name == 'dtype':
+ return numpy.dtype
+ if module == '_codecs' and name == 'encode':
+ return encode
+ if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint':
+ import pytorch_lightning.callbacks
+ return pytorch_lightning.callbacks.model_checkpoint
+ if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint':
+ import pytorch_lightning.callbacks.model_checkpoint
+ return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint
+ if module == "__builtin__" and name == 'set':
+ return set
+
+ # Forbid everything else.
+ raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
+
+
+def check_pt(filename):
+ try:
+
+ # new pytorch format is a zip file
+ with zipfile.ZipFile(filename) as z:
+ with z.open('archive/data.pkl') as file:
+ unpickler = RestrictedUnpickler(file)
+ unpickler.load()
+
+ except zipfile.BadZipfile:
+
+ # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+ with open(filename, "rb") as file:
+ unpickler = RestrictedUnpickler(file)
+ for i in range(5):
+ unpickler.load()
+
+
+def load(filename, *args, **kwargs):
+ from modules import shared
+
+ try:
+ if not shared.cmd_opts.disable_safe_unpickle:
+ check_pt(filename)
+
+ except Exception:
+ print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+ print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
+ print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+ return None
+
+ return unsafe_torch_load(filename, *args, **kwargs)
+
+
+unsafe_torch_load = torch.load
+torch.load = load
diff --git a/modules/scunet_model.py b/modules/scunet_model.py index fb64b740..36a996bf 100644 --- a/modules/scunet_model.py +++ b/modules/scunet_model.py @@ -9,14 +9,12 @@ from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader -from modules.paths import models_path from modules.scunet_model_arch import SCUNet as net class UpscalerScuNET(modules.upscaler.Upscaler): def __init__(self, dirname): self.name = "ScuNET" - self.model_path = os.path.join(models_path, self.name) self.model_name = "ScuNET GAN" self.model_name2 = "ScuNET PSNR" self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth" diff --git a/modules/scunet_model_arch.py b/modules/scunet_model_arch.py index 972a2639..43ca8d36 100644 --- a/modules/scunet_model_arch.py +++ b/modules/scunet_model_arch.py @@ -40,7 +40,7 @@ class WMSA(nn.Module): Returns: attn_mask: should be (1 1 w p p), """ - # supporting sqaure. + # supporting square. attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device) if self.type == 'W': return attn_mask @@ -65,7 +65,7 @@ class WMSA(nn.Module): x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size) h_windows = x.size(1) w_windows = x.size(2) - # sqaure validation + # square validation # assert h_windows == w_windows x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index a6fa890c..437acce4 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -8,7 +8,7 @@ from torch import einsum from torch.nn.functional import silu
import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
from modules.shared import opts, device, cmd_opts
import ldm.modules.attention
@@ -18,23 +18,37 @@ 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.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
+ print("Applying xformers cross attention optimization.")
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+ elif cmd_opts.opt_split_attention_v1:
+ print("Applying v1 cross attention optimization.")
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()):
+ print("Applying cross attention optimization.")
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
def undo_optimizations():
- ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
+ ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
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 = []
@@ -80,10 +94,12 @@ 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):
@@ -92,7 +108,6 @@ 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]
@@ -114,7 +129,6 @@ 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)
@@ -146,19 +160,12 @@ 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
@@ -175,7 +182,8 @@ 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)
@@ -189,7 +197,7 @@ 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 = []
@@ -261,17 +269,29 @@ 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)
- z = outputs.last_hidden_state
+ 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, output_hidden_states=-opts.CLIP_stop_at_last_layers)
+ if opts.CLIP_stop_at_last_layers > 1:
+ z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
+ z = self.wrapped.transformer.text_model.final_layer_norm(z)
+ else:
+ 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()
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index ea4cfdfc..634fb4b2 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -1,22 +1,46 @@ import math
+import sys
+import traceback
+
import torch
from torch import einsum
from ldm.util import default
from einops import rearrange
+from modules import shared
+
+if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_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.loaded_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):
@@ -29,6 +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
@@ -42,8 +67,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None): q_in = self.to_q(x)
context = default(context, x)
- k_in = self.to_k(context) * self.scale
- v_in = self.to_v(context)
+
+ hypernetwork = shared.loaded_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)
+
+ k_in *= self.scale
+
del context, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
@@ -92,6 +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.loaded_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_)
@@ -154,3 +209,16 @@ def cross_attention_attnblock_forward(self, x): h3 += x
return h3
+
+def xformers_attnblock_forward(self, x):
+ try:
+ 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
+ except NotImplementedError:
+ return cross_attention_attnblock_forward(self, x)
diff --git a/modules/sd_models.py b/modules/sd_models.py index 5f992064..e63d3c29 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -5,7 +5,6 @@ from collections import namedtuple import torch
from omegaconf import OmegaConf
-
from ldm.util import instantiate_from_config
from modules import shared, modelloader, devices
@@ -14,7 +13,7 @@ from modules.paths import models_path model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(models_path, model_dir))
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
checkpoints_list = {}
try:
@@ -63,14 +62,20 @@ def list_models(): if os.path.exists(cmd_ckpt):
h = model_hash(cmd_ckpt)
title, short_model_name = modeltitle(cmd_ckpt, h)
- checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
+ checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
shared.opts.data['sd_model_checkpoint'] = title
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
for filename in model_list:
h = model_hash(filename)
title, short_model_name = modeltitle(filename, h)
- checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
+
+ basename, _ = os.path.splitext(filename)
+ config = basename + ".yaml"
+ if not os.path.exists(config):
+ config = shared.cmd_opts.config
+
+ checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
def get_closet_checkpoint_match(searchString):
@@ -116,13 +121,24 @@ def select_checkpoint(): return checkpoint_info
-def load_model_weights(model, checkpoint_file, sd_model_hash):
+def get_state_dict_from_checkpoint(pl_sd):
+ if "state_dict" in pl_sd:
+ return pl_sd["state_dict"]
+
+ return pl_sd
+
+
+def load_model_weights(model, checkpoint_info):
+ checkpoint_file = checkpoint_info.filename
+ sd_model_hash = checkpoint_info.hash
+
print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
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"]
+
+ sd = get_state_dict_from_checkpoint(pl_sd)
model.load_state_dict(sd, strict=False)
@@ -134,17 +150,29 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+ vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+ if os.path.exists(vae_file):
+ print(f"Loading VAE weights from: {vae_file}")
+ vae_ckpt = torch.load(vae_file, map_location="cpu")
+ vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+
+ model.first_stage_model.load_state_dict(vae_dict)
+
model.sd_model_hash = sd_model_hash
- model.sd_model_checkpint = checkpoint_file
+ model.sd_model_checkpoint = checkpoint_file
+ model.sd_checkpoint_info = checkpoint_info
def load_model():
from modules import lowvram, sd_hijack
checkpoint_info = select_checkpoint()
- sd_config = OmegaConf.load(shared.cmd_opts.config)
+ if checkpoint_info.config != shared.cmd_opts.config:
+ print(f"Loading config from: {checkpoint_info.config}")
+
+ sd_config = OmegaConf.load(checkpoint_info.config)
sd_model = instantiate_from_config(sd_config.model)
- load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+ load_model_weights(sd_model, checkpoint_info)
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
@@ -163,9 +191,13 @@ def reload_model_weights(sd_model, info=None): from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
- if sd_model.sd_model_checkpint == checkpoint_info.filename:
+ if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
+ if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+ shared.sd_model = load_model()
+ return shared.sd_model
+
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
else:
@@ -173,7 +205,7 @@ def reload_model_weights(sd_model, info=None): sd_hijack.model_hijack.undo_hijack(sd_model)
- load_model_weights(sd_model, checkpoint_info.filename, checkpoint_info.hash)
+ load_model_weights(sd_model, checkpoint_info)
sd_hijack.model_hijack.hijack(sd_model)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index df17e93c..6e743f7e 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -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
@@ -171,7 +181,7 @@ class VanillaStableDiffusionSampler: self.initialize(p)
- # existing code fails with cetain step counts, like 9
+ # existing code fails with certain step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception:
@@ -194,7 +204,7 @@ class VanillaStableDiffusionSampler: steps = steps or p.steps
- # existing code fails with cetin step counts, like 9
+ # existing code fails with certain step counts, like 9
try:
samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
except Exception:
@@ -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])
+
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
- denoised_uncond = x_out[-batch_size:]
+ 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:
diff --git a/modules/shared.py b/modules/shared.py index 25bb6e6c..1995a99a 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models
import modules.styles
import modules.devices as devices
-from modules import sd_samplers
+from modules import sd_samplers, hypernetwork
from modules.paths import models_path, script_path, sd_path
sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -43,6 +43,9 @@ 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("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
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")
@@ -62,6 +65,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
cmd_opts = parser.parse_args()
@@ -73,11 +77,15 @@ 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.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
+loaded_hypernetwork = None
+
class State:
+ skipped = False
interrupted = False
job = ""
job_no = 0
@@ -90,6 +98,9 @@ class State: current_image_sampling_step = 0
textinfo = None
+ def skip(self):
+ self.skipped = True
+
def interrupt(self):
self.interrupted = True
@@ -112,8 +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():
@@ -122,18 +131,19 @@ def realesrgan_models_names(): class OptionInfo:
- def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
+ def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
self.onchange = onchange
self.section = None
+ self.show_on_main_page = show_on_main_page
-def options_section(section_identifer, options_dict):
+def options_section(section_identifier, options_dict):
for k, v in options_dict.items():
- v.section = section_identifer
+ v.section = section_identifier
return options_dict
@@ -205,7 +215,8 @@ options_templates.update(options_section(('system', "System"), { }))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
- "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
+ "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
+ "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@@ -214,6 +225,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+ 'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
}))
@@ -228,13 +240,14 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('ui', "User interface"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
- "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+ "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+ "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
"font": OptionInfo("", "Font for image grids that have text"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
- "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+ "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
}))
diff --git a/modules/swinir_model.py b/modules/swinir_model.py index 9bd454c6..fbd11f84 100644 --- a/modules/swinir_model.py +++ b/modules/swinir_model.py @@ -8,7 +8,6 @@ from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader -from modules.paths import models_path from modules.shared import cmd_opts, opts, device from modules.swinir_model_arch import SwinIR as net from modules.upscaler import Upscaler, UpscalerData @@ -25,7 +24,6 @@ class UpscalerSwinIR(Upscaler): "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ "-L_x4_GAN.pth " self.model_name = "SwinIR 4x" - self.model_path = os.path.join(models_path, self.name) self.user_path = dirname super().__init__() scalers = [] diff --git a/modules/swinir_model_arch.py b/modules/swinir_model_arch.py index 461fb354..863f42db 100644 --- a/modules/swinir_model_arch.py +++ b/modules/swinir_model_arch.py @@ -166,7 +166,7 @@ class SwinTransformerBlock(nn.Module): Args: dim (int): Number of input channels. - input_resolution (tuple[int]): Input resulotion. + input_resolution (tuple[int]): Input resolution. num_heads (int): Number of attention heads. window_size (int): Window size. shift_size (int): Shift size for SW-MSA. diff --git a/modules/ui.py b/modules/ui.py index 21c9236b..1c0d35fe 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -25,6 +25,8 @@ import gradio.routes from modules import sd_hijack
from modules.paths import script_path
from modules.shared import opts, cmd_opts
+if cmd_opts.deepdanbooru:
+ from modules.deepbooru import get_deepbooru_tags
import modules.shared as shared
from modules.sd_samplers import samplers, samplers_for_img2img
from modules.sd_hijack import model_hijack
@@ -39,7 +41,7 @@ from modules.images import save_image import modules.textual_inversion.ui
import modules.images_history as img_his
-# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
+# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
@@ -99,11 +101,12 @@ def send_gradio_gallery_to_image(x): return image_from_url_text(x[0])
-def save_files(js_data, images, index):
+def save_files(js_data, images, do_make_zip, index):
import csv
filenames = []
+ fullfns = []
- #quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
+ #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
class MyObject:
def __init__(self, d=None):
if d is not None:
@@ -138,14 +141,29 @@ def save_files(js_data, images, index): is_grid = image_index < p.index_of_first_image
i = 0 if is_grid else (image_index - p.index_of_first_image)
- fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+ fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
filename = os.path.relpath(fullfn, path)
filenames.append(filename)
+ fullfns.append(fullfn)
+ if txt_fullfn:
+ filenames.append(os.path.basename(txt_fullfn))
+ fullfns.append(txt_fullfn)
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
- return '', '', plaintext_to_html(f"Saved: {filenames[0]}")
+ # Make Zip
+ if do_make_zip:
+ zip_filepath = os.path.join(path, "images.zip")
+
+ from zipfile import ZipFile
+ with ZipFile(zip_filepath, "w") as zip_file:
+ for i in range(len(fullfns)):
+ with open(fullfns[i], mode="rb") as f:
+ zip_file.writestr(filenames[i], f.read())
+ fullfns.insert(0, zip_filepath)
+
+ return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
def wrap_gradio_call(func, extra_outputs=None):
@@ -192,6 +210,7 @@ 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
@@ -292,6 +311,11 @@ def interrogate(image): return gr_show(True) if prompt is None else prompt
+def interrogate_deepbooru(image):
+ prompt = get_deepbooru_tags(image)
+ return gr_show(True) if prompt is None else prompt
+
+
def create_seed_inputs():
with gr.Row():
with gr.Box():
@@ -412,24 +436,36 @@ 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=[],
outputs=[],
)
- with gr.Row():
+ with gr.Row(scale=1):
if is_img2img:
- interrogate = gr.Button('Interrogate', elem_id="interrogate")
+ interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+ if cmd_opts.deepdanbooru:
+ deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
+ else:
+ deepbooru = None
else:
interrogate = None
+ deepbooru = None
prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
save_style = gr.Button('Create style', elem_id="style_create")
- return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, prompt_style_apply, save_style, paste, token_counter, token_button
+ return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -458,7 +494,7 @@ def create_ui(wrap_gradio_gpu_call): import modules.txt2img
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
- txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
+ txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=False)
dummy_component = gr.Label(visible=False)
with gr.Row(elem_id='txt2img_progress_row'):
@@ -513,6 +549,12 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
+ with gr.Row():
+ do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+ with gr.Row():
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
with gr.Group():
html_info = gr.HTML()
generation_info = gr.Textbox(visible=False)
@@ -563,13 +605,15 @@ def create_ui(wrap_gradio_gpu_call): save.click(
fn=wrap_gradio_call(save_files),
- _js="(x, y, z) => [x, y, selected_gallery_index()]",
+ _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
inputs=[
generation_info,
txt2img_gallery,
+ do_make_zip,
html_info,
],
outputs=[
+ download_files,
html_info,
html_info,
html_info,
@@ -611,7 +655,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Blocks(analytics_enabled=False) as img2img_interface:
- img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
+ img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, paste, token_counter, token_button = create_toprow(is_img2img=True)
with gr.Row(elem_id='img2img_progress_row'):
with gr.Column(scale=1):
@@ -695,6 +739,12 @@ def create_ui(wrap_gradio_gpu_call): button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
+ with gr.Row():
+ do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+ with gr.Row():
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
with gr.Group():
html_info = gr.HTML()
generation_info = gr.Textbox(visible=False)
@@ -769,15 +819,24 @@ def create_ui(wrap_gradio_gpu_call): outputs=[img2img_prompt],
)
+ if cmd_opts.deepdanbooru:
+ img2img_deepbooru.click(
+ fn=interrogate_deepbooru,
+ inputs=[init_img],
+ outputs=[img2img_prompt],
+ )
+
save.click(
fn=wrap_gradio_call(save_files),
- _js="(x, y, z) => [x, y, selected_gallery_index()]",
+ _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
inputs=[
generation_info,
img2img_gallery,
- html_info
+ do_make_zip,
+ html_info,
],
outputs=[
+ download_files,
html_info,
html_info,
html_info,
@@ -941,7 +1000,7 @@ 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'):
@@ -975,9 +1034,9 @@ def create_ui(wrap_gradio_gpu_call): process_dst = gr.Textbox(label='Destination directory')
with gr.Row():
- process_flip = gr.Checkbox(label='Flip')
- process_split = gr.Checkbox(label='Split into two')
- process_caption = gr.Checkbox(label='Add caption')
+ process_flip = gr.Checkbox(label='Create flipped copies')
+ process_split = gr.Checkbox(label='Split oversized images into two')
+ process_caption = gr.Checkbox(label='Use BLIP caption as filename')
with gr.Row():
with gr.Column(scale=3):
@@ -1097,6 +1156,15 @@ def create_ui(wrap_gradio_gpu_call): component_dict = {}
def open_folder(f):
+ if not os.path.isdir(f):
+ print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+ return
+
if not shared.cmd_opts.hide_ui_dir_config:
path = os.path.normpath(f)
if platform.system() == "Windows":
@@ -1110,10 +1178,13 @@ def create_ui(wrap_gradio_gpu_call): changed = 0
for key, value, comp in zip(opts.data_labels.keys(), args, components):
- if not opts.same_type(value, opts.data_labels[key].default):
- return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
+ if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
+ return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
for key, value, comp in zip(opts.data_labels.keys(), args, components):
+ if comp == dummy_component:
+ continue
+
comp_args = opts.data_labels[key].component_args
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
continue
@@ -1140,6 +1211,21 @@ def create_ui(wrap_gradio_gpu_call): images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
+ def run_settings_single(value, key):
+ if not opts.same_type(value, opts.data_labels[key].default):
+ return gr.update(visible=True), opts.dumpjson()
+
+ oldval = opts.data.get(key, None)
+ opts.data[key] = value
+
+ if oldval != value:
+ if opts.data_labels[key].onchange is not None:
+ opts.data_labels[key].onchange()
+
+ opts.save(shared.config_filename)
+
+ return gr.update(value=value), opts.dumpjson()
+
with gr.Blocks(analytics_enabled=False) as settings_interface:
settings_submit = gr.Button(value="Apply settings", variant='primary')
result = gr.HTML()
@@ -1147,6 +1233,8 @@ def create_ui(wrap_gradio_gpu_call): settings_cols = 3
items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
+ quicksettings_list = []
+
cols_displayed = 0
items_displayed = 0
previous_section = None
@@ -1169,10 +1257,14 @@ def create_ui(wrap_gradio_gpu_call): gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
- component = create_setting_component(k)
- component_dict[k] = component
- components.append(component)
- items_displayed += 1
+ if item.show_on_main_page:
+ quicksettings_list.append((i, k, item))
+ components.append(dummy_component)
+ else:
+ component = create_setting_component(k)
+ component_dict[k] = component
+ components.append(component)
+ items_displayed += 1
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
request_notifications.click(
@@ -1186,7 +1278,6 @@ def create_ui(wrap_gradio_gpu_call): reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
-
def reload_scripts():
modules.scripts.reload_script_body_only()
@@ -1235,7 +1326,11 @@ def create_ui(wrap_gradio_gpu_call): css += css_hide_progressbar
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
-
+ with gr.Row(elem_id="quicksettings"):
+ for i, k, item in quicksettings_list:
+ component = create_setting_component(k)
+ component_dict[k] = component
+
settings_interface.gradio_ref = demo
with gr.Tabs() as tabs:
@@ -1252,7 +1347,16 @@ def create_ui(wrap_gradio_gpu_call): inputs=components,
outputs=[result, text_settings],
)
-
+
+ for i, k, item in quicksettings_list:
+ component = component_dict[k]
+
+ component.change(
+ fn=lambda value, k=k: run_settings_single(value, key=k),
+ inputs=[component],
+ outputs=[component, text_settings],
+ )
+
def modelmerger(*args):
try:
results = modules.extras.run_modelmerger(*args)
diff --git a/modules/upscaler.py b/modules/upscaler.py index d9d7c5e2..6ab2fb40 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -36,10 +36,11 @@ class Upscaler: self.half = not modules.shared.cmd_opts.no_half self.pre_pad = 0 self.mod_scale = None - if self.name is not None and create_dirs: + + if self.model_path is None and self.name: self.model_path = os.path.join(models_path, self.name) - if not os.path.exists(self.model_path): - os.makedirs(self.model_path) + if self.model_path and create_dirs: + os.makedirs(self.model_path, exist_ok=True) try: import cv2 diff --git a/requirements.txt b/requirements.txt index 631fe616..81641d68 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,3 +23,4 @@ resize-right torchdiffeq
kornia
lark
+functorch
diff --git a/requirements_versions.txt b/requirements_versions.txt index fdff2687..fec3e9d5 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -22,3 +22,4 @@ resize-right==0.0.2 torchdiffeq==0.2.3
kornia==0.6.7
lark==1.1.2
+functorch==0.2.1
diff --git a/scripts/prompts_from_file.py b/scripts/prompts_from_file.py index 513d9a1c..b24f1a80 100644 --- a/scripts/prompts_from_file.py +++ b/scripts/prompts_from_file.py @@ -10,7 +10,6 @@ from modules.processing import Processed, process_images from PIL import Image
from modules.shared import opts, cmd_opts, state
-
class Script(scripts.Script):
def title(self):
return "Prompts from file or textbox"
@@ -29,6 +28,9 @@ class Script(scripts.Script): checkbox_txt.change(fn=lambda x: [gr.File.update(visible = not x), gr.TextArea.update(visible = x)], inputs=[checkbox_txt], outputs=[file, prompt_txt])
return [checkbox_txt, file, prompt_txt]
+ def on_show(self, checkbox_txt, file, prompt_txt):
+ return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
+
def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
if (checkbox_txt):
lines = [x.strip() for x in prompt_txt.splitlines()]
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 6344e612..771eb8e4 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -10,8 +10,8 @@ import numpy as np import modules.scripts as scripts
import gradio as gr
-from modules import images
-from modules.processing import process_images, Processed
+from modules import images, hypernetwork
+from modules.processing import process_images, Processed, get_correct_sampler
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
import modules.sd_samplers
@@ -56,15 +56,17 @@ def apply_order(p, x, xs): p.prompt = prompt_tmp + p.prompt
-samplers_dict = {}
-for i, sampler in enumerate(modules.sd_samplers.samplers):
- samplers_dict[sampler.name.lower()] = i
- for alias in sampler.aliases:
- samplers_dict[alias.lower()] = i
+def build_samplers_dict(p):
+ samplers_dict = {}
+ for i, sampler in enumerate(get_correct_sampler(p)):
+ samplers_dict[sampler.name.lower()] = i
+ for alias in sampler.aliases:
+ samplers_dict[alias.lower()] = i
+ return samplers_dict
def apply_sampler(p, x, xs):
- sampler_index = samplers_dict.get(x.lower(), None)
+ sampler_index = build_samplers_dict(p).get(x.lower(), None)
if sampler_index is None:
raise RuntimeError(f"Unknown sampler: {x}")
@@ -77,6 +79,14 @@ def apply_checkpoint(p, x, xs): modules.sd_models.reload_model_weights(shared.sd_model, info)
+def apply_hypernetwork(p, x, xs):
+ hypernetwork.load_hypernetwork(x)
+
+
+def apply_clip_skip(p, x, xs):
+ opts.data["CLIP_stop_at_last_layers"] = x
+
+
def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
@@ -122,11 +132,13 @@ axis_options = [ AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
AxisOption("Sampler", str, apply_sampler, format_value),
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
+ AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
+ AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label), # as it is now all AxisOptionImg2Img items must go after AxisOption ones
]
@@ -137,7 +149,7 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend): ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
- first_pocessed = None
+ first_processed = None
state.job_count = len(xs) * len(ys) * p.n_iter
@@ -146,8 +158,8 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend): state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
processed = cell(x, y)
- if first_pocessed is None:
- first_pocessed = processed
+ if first_processed is None:
+ first_processed = processed
try:
res.append(processed.images[0])
@@ -158,9 +170,9 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend): if draw_legend:
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
- first_pocessed.images = [grid]
+ first_processed.images = [grid]
- return first_pocessed
+ return first_processed
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -190,8 +202,11 @@ class Script(scripts.Script): return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
- modules.processing.fix_seed(p)
+ if not no_fixed_seeds:
+ modules.processing.fix_seed(p)
+
p.batch_size = 1
+ CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
def process_axis(opt, vals):
if opt.label == 'Nothing':
@@ -206,7 +221,6 @@ class Script(scripts.Script): m = re_range.fullmatch(val)
mc = re_range_count.fullmatch(val)
if m is not None:
-
start = int(m.group(1))
end = int(m.group(2))+1
step = int(m.group(3)) if m.group(3) is not None else 1
@@ -248,6 +262,17 @@ class Script(scripts.Script): valslist = list(permutations(valslist))
valslist = [opt.type(x) for x in valslist]
+
+ # Confirm options are valid before starting
+ if opt.label == "Sampler":
+ samplers_dict = build_samplers_dict(p)
+ for sampler_val in valslist:
+ if sampler_val.lower() not in samplers_dict.keys():
+ raise RuntimeError(f"Unknown sampler: {sampler_val}")
+ elif opt.label == "Checkpoint name":
+ for ckpt_val in valslist:
+ if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
+ raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
return valslist
@@ -300,4 +325,8 @@ class Script(scripts.Script): # restore checkpoint in case it was changed by axes
modules.sd_models.reload_model_weights(shared.sd_model)
+ hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
+
+ opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers
+
return processed
@@ -103,7 +103,12 @@ #style_apply, #style_create, #interrogate{
margin: 0.75em 0.25em 0.25em 0.25em;
- min-width: 3em;
+ min-width: 5em;
+}
+
+#style_apply, #style_create, #deepbooru{
+ margin: 0.75em 0.25em 0.25em 0.25em;
+ min-width: 5em;
}
#style_pos_col, #style_neg_col{
@@ -393,10 +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;
}
@@ -410,4 +425,41 @@ input[type="range"]{ #img2img_image div.h-60{
height: 480px;
-}
\ No newline at end of file +}
+
+#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;
+}
+
+#quicksettings > div{
+ border: none;
+ background: none;
+}
+
+#quicksettings > div > div{
+ max-width: 32em;
+ padding: 0;
+}
diff --git a/txt2img_Screenshot.png b/txt2img_Screenshot.png Binary files differindex fedd538e..6e2759a4 100644 --- a/txt2img_Screenshot.png +++ b/txt2img_Screenshot.png @@ -5,6 +5,8 @@ import importlib import signal
import threading
+from fastapi.middleware.gzip import GZipMiddleware
+
from modules.paths import script_path
from modules import devices, sd_samplers
@@ -58,6 +60,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): shared.state.current_latent = None
shared.state.current_image = None
shared.state.current_image_sampling_step = 0
+ shared.state.skipped = False
shared.state.interrupted = False
shared.state.textinfo = None
@@ -79,6 +82,9 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts")) shared.sd_model = modules.sd_models.load_model()
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
+loaded_hypernetwork = modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)
+shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+
def webui():
# make the program just exit at ctrl+c without waiting for anything
@@ -92,7 +98,7 @@ def webui(): demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
- demo.launch(
+ app,local_url,share_url = demo.launch(
share=cmd_opts.share,
server_name="0.0.0.0" if cmd_opts.listen else None,
server_port=cmd_opts.port,
@@ -101,6 +107,8 @@ def webui(): inbrowser=cmd_opts.autolaunch,
prevent_thread_lock=True
)
+
+ app.add_middleware(GZipMiddleware,minimum_size=1000)
while 1:
time.sleep(0.5)
|
