diff options
| author | Karun <karun.ellango7@gmail.com> | 2023-03-25 05:12:55 -0400 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-03-25 05:12:55 -0400 |
| commit | 63a2f8d8225228a52b3ca7f19d2ba1fd07a6234b (patch) | |
| tree | 9a7c38070d83b409895704125525dfc70cc21215 | |
| parent | ca2b8faa83076a21dd14c974f03f88eb6da57485 (diff) | |
| parent | 70615448b2ef3285dba9bb1992974cb1eaf10995 (diff) | |
Merge branch 'master' into master
64 files changed, 2728 insertions, 963 deletions
@@ -157,5 +157,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
+- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index cb8f1d36..7c371deb 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -3,7 +3,9 @@ import os import re
import torch
-from modules import shared, devices, sd_models
+from modules import shared, devices, sd_models, errors
+
+metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
re_digits = re.compile(r"\d+")
re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
@@ -43,6 +45,23 @@ class LoraOnDisk: def __init__(self, name, filename):
self.name = name
self.filename = filename
+ self.metadata = {}
+
+ _, ext = os.path.splitext(filename)
+ if ext.lower() == ".safetensors":
+ try:
+ self.metadata = sd_models.read_metadata_from_safetensors(filename)
+ except Exception as e:
+ errors.display(e, f"reading lora {filename}")
+
+ if self.metadata:
+ m = {}
+ for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
+ m[k] = v
+
+ self.metadata = m
+
+ self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text
class LoraModule:
@@ -159,6 +178,7 @@ def load_loras(names, multipliers=None): def lora_forward(module, input, res):
+ input = devices.cond_cast_unet(input)
if len(loaded_loras) == 0:
return res
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py index 22cabcb0..68b11332 100644 --- a/extensions-builtin/Lora/ui_extra_networks_lora.py +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -15,21 +15,15 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): def list_items(self):
for name, lora_on_disk in lora.available_loras.items():
path, ext = os.path.splitext(lora_on_disk.filename)
- previews = [path + ".png", path + ".preview.png"]
-
- preview = None
- for file in previews:
- if os.path.isfile(file):
- preview = self.link_preview(file)
- break
-
yield {
"name": name,
"filename": path,
- "preview": preview,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
"search_term": self.search_terms_from_path(lora_on_disk.filename),
"prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
- "local_preview": path + ".png",
+ "local_preview": f"{path}.{shared.opts.samples_format}",
+ "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
}
def allowed_directories_for_previews(self):
diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js index 4a85c8eb..f0918e26 100644 --- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js +++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js @@ -89,22 +89,15 @@ function checkBrackets(evt, textArea, counterElt) { function setupBracketChecking(id_prompt, id_counter){ var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); var counter = gradioApp().getElementById(id_counter) + textarea.addEventListener("input", function(evt){ checkBrackets(evt, textarea, counter) }); } -var shadowRootLoaded = setInterval(function() { - var shadowRoot = document.querySelector('gradio-app').shadowRoot; - if(! shadowRoot) return false; - - var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea'); - if(shadowTextArea.length < 1) return false; - - clearInterval(shadowRootLoaded); - +onUiLoaded(function(){ setupBracketChecking('txt2img_prompt', 'txt2img_token_counter') setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter') - setupBracketChecking('img2img_prompt', 'imgimg_token_counter') + setupBracketChecking('img2img_prompt', 'img2img_token_counter') setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter') -}, 1000); +})
\ No newline at end of file diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html index 250bb60d..58250bb7 100644 --- a/html/extra-networks-card.html +++ b/html/extra-networks-card.html @@ -7,6 +7,7 @@ <span style="display:none" class='search_term'>{search_term}</span> </div> <span class='name'>{name}</span> + <span class='description'>{description}</span> </div> </div> diff --git a/html/licenses.html b/html/licenses.html index 570630eb..bc995aa0 100644 --- a/html/licenses.html +++ b/html/licenses.html @@ -417,3 +417,248 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
</pre>
+<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
+<small>Some small amounts of code borrowed and reworked.</small>
+<pre>
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+
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+</pre>
+
+<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
+<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
+<pre>
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+</pre>
\ No newline at end of file diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js index 11bcce1b..06f505b0 100644 --- a/javascript/contextMenus.js +++ b/javascript/contextMenus.js @@ -43,7 +43,7 @@ contextMenuInit = function(){ })
- gradioApp().getRootNode().appendChild(contextMenu)
+ gradioApp().appendChild(contextMenu)
let menuWidth = contextMenu.offsetWidth + 4;
let menuHeight = contextMenu.offsetHeight + 4;
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 619bb1fa..20a5aadf 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,6 +1,6 @@ function keyupEditAttention(event){
let target = event.originalTarget || event.composedPath()[0];
- if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
+ if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
if (! (event.metaKey || event.ctrlKey)) return;
let isPlus = event.key == "ArrowUp"
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index 17bf2000..40818bb4 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -5,12 +5,10 @@ function setupExtraNetworksForTab(tabname){ var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
- var close = gradioApp().getElementById(tabname+'_extra_close')
search.classList.add('search')
tabs.appendChild(search)
tabs.appendChild(refresh)
- tabs.appendChild(close)
search.addEventListener("input", function(evt){
searchTerm = search.value.toLowerCase()
@@ -78,7 +76,7 @@ function cardClicked(tabname, textToAdd, allowNegativePrompt){ var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
- textarea.value = textarea.value + " " + textToAdd
+ textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
}
updateInput(textarea)
@@ -104,4 +102,76 @@ function extraNetworksSearchButton(tabs_id, event){ searchTextarea.value = text
updateInput(searchTextarea)
-}
\ No newline at end of file +}
+
+var globalPopup = null;
+var globalPopupInner = null;
+function popup(contents){
+ if(! globalPopup){
+ globalPopup = document.createElement('div')
+ globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
+ globalPopup.classList.add('global-popup');
+
+ var close = document.createElement('div')
+ close.classList.add('global-popup-close');
+ close.onclick = function(){ globalPopup.style.display = "none"; };
+ close.title = "Close";
+ globalPopup.appendChild(close)
+
+ globalPopupInner = document.createElement('div')
+ globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
+ globalPopupInner.classList.add('global-popup-inner');
+ globalPopup.appendChild(globalPopupInner)
+
+ gradioApp().appendChild(globalPopup);
+ }
+
+ globalPopupInner.innerHTML = '';
+ globalPopupInner.appendChild(contents);
+
+ globalPopup.style.display = "flex";
+}
+
+function extraNetworksShowMetadata(text){
+ elem = document.createElement('pre')
+ elem.classList.add('popup-metadata');
+ elem.textContent = text;
+
+ popup(elem);
+}
+
+function requestGet(url, data, handler, errorHandler){
+ var xhr = new XMLHttpRequest();
+ var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
+ xhr.open("GET", url + "?" + args, true);
+
+ xhr.onreadystatechange = function () {
+ if (xhr.readyState === 4) {
+ if (xhr.status === 200) {
+ try {
+ var js = JSON.parse(xhr.responseText);
+ handler(js)
+ } catch (error) {
+ console.error(error);
+ errorHandler()
+ }
+ } else{
+ errorHandler()
+ }
+ }
+ };
+ var js = JSON.stringify(data);
+ xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(extraPage, cardName){
+ showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+
+ requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
+ if(data && data.metadata){
+ extraNetworksShowMetadata(data.metadata)
+ } else{
+ showError()
+ }
+ }, showError)
+}
diff --git a/javascript/hints.js b/javascript/hints.js index f1199009..b3f3d08d 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -6,6 +6,7 @@ titles = { "GFPGAN": "Restore low quality faces using GFPGAN neural network", "Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help", "DDIM": "Denoising Diffusion Implicit Models - best at inpainting", + "UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models", "DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", "Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)", @@ -17,7 +18,7 @@ titles = { "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", "\u{1f4c2}": "Open images output directory", "\u{1f4be}": "Save style", - "\u{1f5d1}": "Clear prompt", + "\u{1f5d1}\ufe0f": "Clear prompt", "\u{1f4cb}": "Apply selected styles to current prompt", "\u{1f4d2}": "Paste available values into the field", "\u{1f3b4}": "Show extra networks", @@ -39,8 +40,7 @@ titles = { "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", "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.", @@ -70,8 +70,10 @@ titles = { "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", - "Loopback": "Process an image, use it as an input, repeat.", - "Loops": "How many times to repeat processing an image and using it as input for the next iteration", + "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.", + "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.", + "Final denoising strength": "The denoising strength for the final loop of each image in the batch.", + "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.", "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both", diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index aac2ee82..7547e771 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -11,7 +11,7 @@ function showModal(event) { if (modalImage.style.display === 'none') { lb.style.setProperty('background-image', 'url(' + source.src + ')'); } - lb.style.display = "block"; + lb.style.display = "flex"; lb.focus() const tabTxt2Img = gradioApp().getElementById("tab_txt2img") @@ -50,7 +50,7 @@ function updateOnBackgroundChange() { } function modalImageSwitch(offset) { - var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all") + var allgalleryButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item") var galleryButtons = [] allgalleryButtons.forEach(function(elem) { if (elem.parentElement.offsetParent) { @@ -59,7 +59,7 @@ function modalImageSwitch(offset) { }) if (galleryButtons.length > 1) { - var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") + var allcurrentButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item.selected") var currentButton = null allcurrentButtons.forEach(function(elem) { if (elem.parentElement.offsetParent) { @@ -136,37 +136,29 @@ function modalKeyHandler(event) { } } -function showGalleryImage() { - setTimeout(function() { - fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain') - - 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.style.userSelect='none' - - var isFirefox = isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1 - - // For Firefox, listening on click first switched to next image then shows the lightbox. - // If you know how to fix this without switching to mousedown event, please. - // For other browsers the event is click to make it possiblr to drag picture. - var event = isFirefox ? 'mousedown' : 'click' - - e.addEventListener(event, function (evt) { - if(!opts.js_modal_lightbox || evt.button != 0) return; - modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) - evt.preventDefault() - showModal(evt) - }, true); - } - }); - } +function setupImageForLightbox(e) { + if (e.dataset.modded) + return; + + e.dataset.modded = true; + e.style.cursor='pointer' + e.style.userSelect='none' + + var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1 + + // For Firefox, listening on click first switched to next image then shows the lightbox. + // If you know how to fix this without switching to mousedown event, please. + // For other browsers the event is click to make it possiblr to drag picture. + var event = isFirefox ? 'mousedown' : 'click' + + e.addEventListener(event, function (evt) { + if(!opts.js_modal_lightbox || evt.button != 0) return; + + modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) + evt.preventDefault() + showModal(evt) + }, true); - }, 100); } function modalZoomSet(modalImage, enable) { @@ -199,21 +191,21 @@ function modalTileImageToggle(event) { } function galleryImageHandler(e) { - if (e && e.parentElement.tagName == 'BUTTON') { + //if (e && e.parentElement.tagName == 'BUTTON') { e.onclick = showGalleryImage; - } + //} } onUiUpdate(function() { - fullImg_preview = gradioApp().querySelectorAll('img.w-full') + fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img') if (fullImg_preview != null) { - fullImg_preview.forEach(galleryImageHandler); + fullImg_preview.forEach(setupImageForLightbox); } updateOnBackgroundChange(); }) document.addEventListener("DOMContentLoaded", function() { - const modalFragment = document.createDocumentFragment(); + //const modalFragment = document.createDocumentFragment(); const modal = document.createElement('div') modal.onclick = closeModal; modal.id = "lightboxModal"; @@ -277,9 +269,9 @@ document.addEventListener("DOMContentLoaded", function() { modal.appendChild(modalNext) + gradioApp().appendChild(modal) - gradioApp().getRootNode().appendChild(modal) - document.body.appendChild(modalFragment); + document.body.appendChild(modal); }); diff --git a/javascript/notification.js b/javascript/notification.js index 040a3afa..5ae6df24 100644 --- a/javascript/notification.js +++ b/javascript/notification.js @@ -15,7 +15,7 @@ onUiUpdate(function(){ } } - const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] img.h-full.w-full.overflow-hidden'); + const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] img.h-full.w-full.overflow-hidden'); if (galleryPreviews == null) return; diff --git a/javascript/progressbar.js b/javascript/progressbar.js index ff6d757b..4ac9b8db 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -1,78 +1,13 @@ // code related to showing and updating progressbar shown as the image is being made - -galleries = {} -storedGallerySelections = {} -galleryObservers = {} - function rememberGallerySelection(id_gallery){ - storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery) -} -function getGallerySelectedIndex(id_gallery){ - let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') - let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') - - let currentlySelectedIndex = -1 - galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } }) - - return currentlySelectedIndex } -// this is a workaround for https://github.com/gradio-app/gradio/issues/2984 -function check_gallery(id_gallery){ - let gallery = gradioApp().getElementById(id_gallery) - // if gallery has no change, no need to setting up observer again. - if (gallery && galleries[id_gallery] !== gallery){ - galleries[id_gallery] = gallery; - if(galleryObservers[id_gallery]){ - galleryObservers[id_gallery].disconnect(); - } +function getGallerySelectedIndex(id_gallery){ - storedGallerySelections[id_gallery] = -1 - - galleryObservers[id_gallery] = new MutationObserver(function (){ - let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') - let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') - let currentlySelectedIndex = getGallerySelectedIndex(id_gallery) - prevSelectedIndex = storedGallerySelections[id_gallery] - storedGallerySelections[id_gallery] = -1 - - if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) { - // automatically re-open previously selected index (if exists) - activeElement = gradioApp().activeElement; - let scrollX = window.scrollX; - let scrollY = window.scrollY; - - galleryButtons[prevSelectedIndex].click(); - showGalleryImage(); - - // When the gallery button is clicked, it gains focus and scrolls itself into view - // We need to scroll back to the previous position - setTimeout(function (){ - window.scrollTo(scrollX, scrollY); - }, 50); - - if(activeElement){ - // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it - // if someone has a better solution please by all means - setTimeout(function (){ - activeElement.focus({ - preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it - }) - }, 1); - } - } - }) - galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false }) - } } -onUiUpdate(function(){ - check_gallery('txt2img_gallery') - check_gallery('img2img_gallery') -}) - function request(url, data, handler, errorHandler){ var xhr = new XMLHttpRequest(); var url = url; @@ -139,7 +74,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre var divProgress = document.createElement('div') divProgress.className='progressDiv' - divProgress.style.display = opts.show_progressbar ? "" : "none" + divProgress.style.display = opts.show_progressbar ? "block" : "none" var divInner = document.createElement('div') divInner.className='progress' diff --git a/javascript/ui.js b/javascript/ui.js index b7a8268a..fcaf5608 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -86,7 +86,7 @@ function get_tab_index(tabId){ var res = 0 gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){ - if(button.className.indexOf('bg-white') != -1) + if(button.className.indexOf('selected') != -1) res = i }) @@ -255,7 +255,6 @@ onUiUpdate(function(){ } prompt.parentElement.insertBefore(counter, prompt) - counter.classList.add("token-counter") prompt.parentElement.style.position = "relative" promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); } @@ -8,6 +8,14 @@ import platform import argparse
import json
+parser = argparse.ArgumentParser(add_help=False)
+parser.add_argument("--ui-settings-file", type=str, default='config.json')
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.realpath(__file__)))
+args, _ = parser.parse_known_args(sys.argv)
+
+script_path = os.path.dirname(__file__)
+data_path = args.data_dir
+
dir_repos = "repositories"
dir_extensions = "extensions"
python = sys.executable
@@ -122,7 +130,7 @@ def is_installed(package): def repo_dir(name):
- return os.path.join(dir_repos, name)
+ return os.path.join(script_path, dir_repos, name)
def run_python(code, desc=None, errdesc=None):
@@ -161,7 +169,17 @@ def git_clone(url, dir, name, commithash=None): if commithash is not None:
run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
-
+
+def git_pull_recursive(dir):
+ for subdir, _, _ in os.walk(dir):
+ if os.path.exists(os.path.join(subdir, '.git')):
+ try:
+ output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
+ print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
+ except subprocess.CalledProcessError as e:
+ print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
+
+
def version_check(commit):
try:
import requests
@@ -205,7 +223,7 @@ def list_extensions(settings_file): disabled_extensions = set(settings.get('disabled_extensions', []))
- return [x for x in os.listdir(dir_extensions) if x not in disabled_extensions]
+ return [x for x in os.listdir(os.path.join(data_path, dir_extensions)) if x not in disabled_extensions]
def run_extensions_installers(settings_file):
@@ -213,7 +231,7 @@ def run_extensions_installers(settings_file): return
for dirname_extension in list_extensions(settings_file):
- run_extension_installer(os.path.join(dir_extensions, dirname_extension))
+ run_extension_installer(os.path.join(data_path, dir_extensions, dirname_extension))
def prepare_environment():
@@ -242,11 +260,8 @@ def prepare_environment(): sys.argv += shlex.split(commandline_args)
- parser = argparse.ArgumentParser(add_help=False)
- parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
- args, _ = parser.parse_known_args(sys.argv)
-
sys.argv, _ = extract_arg(sys.argv, '-f')
+ sys.argv, update_all_extensions = extract_arg(sys.argv, '--update-all-extensions')
sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
sys.argv, skip_python_version_check = extract_arg(sys.argv, '--skip-python-version-check')
sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
@@ -295,7 +310,7 @@ def prepare_environment(): if not is_installed("pyngrok") and ngrok:
run_pip("install pyngrok", "ngrok")
- os.makedirs(dir_repos, exist_ok=True)
+ os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
@@ -304,14 +319,19 @@ def prepare_environment(): git_clone(blip_repo, 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 \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
- run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+ if not os.path.isfile(requirements_file):
+ requirements_file = os.path.join(script_path, requirements_file)
+ run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
run_extensions_installers(settings_file=args.ui_settings_file)
if update_check:
version_check(commit)
+
+ if update_all_extensions:
+ git_pull_recursive(os.path.join(data_path, dir_extensions))
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
@@ -327,7 +347,7 @@ def tests(test_dir): sys.argv.append("--api")
if "--ckpt" not in sys.argv:
sys.argv.append("--ckpt")
- sys.argv.append("./test/test_files/empty.pt")
+ sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
if "--skip-torch-cuda-test" not in sys.argv:
sys.argv.append("--skip-torch-cuda-test")
if "--disable-nan-check" not in sys.argv:
@@ -336,7 +356,7 @@ def tests(test_dir): print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
os.environ['COMMANDLINE_ARGS'] = ""
- with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
+ with open(os.path.join(script_path, 'test/stdout.txt'), "w", encoding="utf8") as stdout, open(os.path.join(script_path, 'test/stderr.txt'), "w", encoding="utf8") as stderr:
proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)
import test.server_poll
diff --git a/modules/api/api.py b/modules/api/api.py index 5a9ac5f1..f52f7fef 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -18,7 +18,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_ from modules.textual_inversion.preprocess import preprocess from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from PIL import PngImagePlugin,Image -from modules.sd_models import checkpoints_list +from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights from modules.sd_models_config import find_checkpoint_config_near_filename from modules.realesrgan_model import get_realesrgan_models from modules import devices @@ -150,6 +150,9 @@ class Api: self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse) self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse) self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse) + self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList) def add_api_route(self, path: str, endpoint, **kwargs): if shared.cmd_opts.api_auth: @@ -163,47 +166,98 @@ class Api: raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) - def get_script(self, script_name, script_runner): - if script_name is None: + def get_selectable_script(self, script_name, script_runner): + if script_name is None or script_name == "": return None, None - if not script_runner.scripts: - script_runner.initialize_scripts(False) - ui.create_ui() - script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) script = script_runner.selectable_scripts[script_idx] return script, script_idx + + def get_scripts_list(self): + t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles] + i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles] + + return ScriptsList(txt2img = t2ilist, img2img = i2ilist) + + def get_script(self, script_name, script_runner): + if script_name is None or script_name == "": + return None, None + + script_idx = script_name_to_index(script_name, script_runner.scripts) + return script_runner.scripts[script_idx] + + def init_script_args(self, request, selectable_scripts, selectable_idx, script_runner): + #find max idx from the scripts in runner and generate a none array to init script_args + last_arg_index = 1 + for script in script_runner.scripts: + if last_arg_index < script.args_to: + last_arg_index = script.args_to + # None everywhere except position 0 to initialize script args + script_args = [None]*last_arg_index + # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run() + if selectable_scripts: + script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args + script_args[0] = selectable_idx + 1 + else: + # when [0] = 0 no selectable script to run + script_args[0] = 0 + + # Now check for always on scripts + if request.alwayson_scripts and (len(request.alwayson_scripts) > 0): + for alwayson_script_name in request.alwayson_scripts.keys(): + alwayson_script = self.get_script(alwayson_script_name, script_runner) + if alwayson_script == None: + raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found") + # Selectable script in always on script param check + if alwayson_script.alwayson == False: + raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") + # always on script with no arg should always run so you don't really need to add them to the requests + if "args" in request.alwayson_scripts[alwayson_script_name]: + script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"] + return script_args def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): - script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img) + script_runner = scripts.scripts_txt2img + if not script_runner.scripts: + script_runner.initialize_scripts(False) + ui.create_ui() + selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) - populate = txt2imgreq.copy(update={ # Override __init__ params + populate = txt2imgreq.copy(update={ # Override __init__ params "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), - "do_not_save_samples": True, - "do_not_save_grid": True - } - ) + "do_not_save_samples": not txt2imgreq.save_images, + "do_not_save_grid": not txt2imgreq.save_images, + }) if populate.sampler_name: populate.sampler_index = None # prevent a warning later on args = vars(populate) args.pop('script_name', None) + args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them + args.pop('alwayson_scripts', None) + + script_args = self.init_script_args(txt2imgreq, selectable_scripts, selectable_script_idx, script_runner) + + send_images = args.pop('send_images', True) + args.pop('save_images', None) with self.queue_lock: p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) + p.scripts = script_runner + p.outpath_grids = opts.outdir_txt2img_grids + p.outpath_samples = opts.outdir_txt2img_samples shared.state.begin() - if script is not None: - p.outpath_grids = opts.outdir_txt2img_grids - p.outpath_samples = opts.outdir_txt2img_samples - p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args - processed = scripts.scripts_txt2img.run(p, *p.script_args) + if selectable_scripts != None: + p.script_args = script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here else: + p.script_args = tuple(script_args) # Need to pass args as tuple here processed = process_images(p) shared.state.end() - b64images = list(map(encode_pil_to_base64, processed.images)) + b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) @@ -212,41 +266,53 @@ class Api: if init_images is None: raise HTTPException(status_code=404, detail="Init image not found") - script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img) - mask = img2imgreq.mask if mask: mask = decode_base64_to_image(mask) - populate = img2imgreq.copy(update={ # Override __init__ params + script_runner = scripts.scripts_img2img + if not script_runner.scripts: + script_runner.initialize_scripts(True) + ui.create_ui() + selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) + + populate = img2imgreq.copy(update={ # Override __init__ params "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), - "do_not_save_samples": True, - "do_not_save_grid": True, - "mask": mask - } - ) + "do_not_save_samples": not img2imgreq.save_images, + "do_not_save_grid": not img2imgreq.save_images, + "mask": mask, + }) if populate.sampler_name: populate.sampler_index = None # prevent a warning later on args = vars(populate) args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine. args.pop('script_name', None) + args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them + args.pop('alwayson_scripts', None) + + script_args = self.init_script_args(img2imgreq, selectable_scripts, selectable_script_idx, script_runner) + + send_images = args.pop('send_images', True) + args.pop('save_images', None) with self.queue_lock: p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) p.init_images = [decode_base64_to_image(x) for x in init_images] + p.scripts = script_runner + p.outpath_grids = opts.outdir_img2img_grids + p.outpath_samples = opts.outdir_img2img_samples shared.state.begin() - if script is not None: - p.outpath_grids = opts.outdir_img2img_grids - p.outpath_samples = opts.outdir_img2img_samples - p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args - processed = scripts.scripts_img2img.run(p, *p.script_args) + if selectable_scripts != None: + p.script_args = script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here else: + p.script_args = tuple(script_args) # Need to pass args as tuple here processed = process_images(p) shared.state.end() - b64images = list(map(encode_pil_to_base64, processed.images)) + b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else [] if not img2imgreq.include_init_images: img2imgreq.init_images = None @@ -348,6 +414,16 @@ class Api: return {} + def unloadapi(self): + unload_model_weights() + + return {} + + def reloadapi(self): + reload_model_weights() + + return {} + def skip(self): shared.state.skip() diff --git a/modules/api/models.py b/modules/api/models.py index cba43d3b..4a70f440 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -14,8 +14,8 @@ API_NOT_ALLOWED = [ "outpath_samples", "outpath_grids", "sampler_index", - "do_not_save_samples", - "do_not_save_grid", + # "do_not_save_samples", + # "do_not_save_grid", "extra_generation_params", "overlay_images", "do_not_reload_embeddings", @@ -100,13 +100,31 @@ class PydanticModelGenerator: StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img, - [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}] + [ + {"key": "sampler_index", "type": str, "default": "Euler"}, + {"key": "script_name", "type": str, "default": None}, + {"key": "script_args", "type": list, "default": []}, + {"key": "send_images", "type": bool, "default": True}, + {"key": "save_images", "type": bool, "default": False}, + {"key": "alwayson_scripts", "type": dict, "default": {}}, + ] ).generate_model() StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingImg2Img", StableDiffusionProcessingImg2Img, - [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}] + [ + {"key": "sampler_index", "type": str, "default": "Euler"}, + {"key": "init_images", "type": list, "default": None}, + {"key": "denoising_strength", "type": float, "default": 0.75}, + {"key": "mask", "type": str, "default": None}, + {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, + {"key": "script_name", "type": str, "default": None}, + {"key": "script_args", "type": list, "default": []}, + {"key": "send_images", "type": bool, "default": True}, + {"key": "save_images", "type": bool, "default": False}, + {"key": "alwayson_scripts", "type": dict, "default": {}}, + ] ).generate_model() class TextToImageResponse(BaseModel): @@ -267,3 +285,7 @@ class EmbeddingsResponse(BaseModel): class MemoryResponse(BaseModel): ram: dict = Field(title="RAM", description="System memory stats") cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats") + +class ScriptsList(BaseModel): + txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)") + img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
\ No newline at end of file diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index 01fb7bd8..8d84bbc9 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -55,7 +55,7 @@ def setup_model(dirname): if self.net is not None and self.face_helper is not None:
self.net.to(devices.device_codeformer)
return self.net, self.face_helper
- model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth')
+ model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
if len(model_paths) != 0:
ckpt_path = model_paths[0]
else:
diff --git a/modules/extensions.py b/modules/extensions.py index 3eef9eaf..ed4b58fe 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -66,7 +66,7 @@ class Extension: def check_updates(self):
repo = git.Repo(self.path)
- for fetch in repo.remote().fetch("--dry-run"):
+ for fetch in repo.remote().fetch(dry_run=True):
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
self.status = "behind"
@@ -79,8 +79,8 @@ class Extension: repo = git.Repo(self.path)
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
- repo.git.fetch('--all')
- repo.git.reset('--hard', 'origin')
+ repo.git.fetch(all=True)
+ repo.git.reset('origin', hard=True)
def list_extensions():
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 89dc23bf..6df76858 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -23,13 +23,14 @@ registered_param_bindings = [] class ParamBinding:
- def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None):
+ def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
self.paste_button = paste_button
self.tabname = tabname
self.source_text_component = source_text_component
self.source_image_component = source_image_component
self.source_tabname = source_tabname
self.override_settings_component = override_settings_component
+ self.paste_field_names = paste_field_names
def reset():
@@ -134,7 +135,7 @@ def connect_paste_params_buttons(): connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
if binding.source_tabname is not None and fields is not None:
- paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
+ paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names
binding.paste_button.click(
fn=lambda *x: x,
inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
@@ -288,6 +289,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model settings_map = {}
+
+
infotext_to_setting_name_mapping = [
('Clip skip', 'CLIP_stop_at_last_layers', ),
('Conditional mask weight', 'inpainting_mask_weight'),
@@ -296,7 +299,11 @@ infotext_to_setting_name_mapping = [ ('Noise multiplier', 'initial_noise_multiplier'),
('Eta', 'eta_ancestral'),
('Eta DDIM', 'eta_ddim'),
- ('Discard penultimate sigma', 'always_discard_next_to_last_sigma')
+ ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'),
+ ('UniPC variant', 'uni_pc_variant'),
+ ('UniPC skip type', 'uni_pc_skip_type'),
+ ('UniPC order', 'uni_pc_order'),
+ ('UniPC lower order final', 'uni_pc_lower_order_final'),
]
@@ -394,9 +401,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, button.click(
fn=paste_func,
- _js=f"recalculate_prompts_{tabname}",
inputs=[input_comp],
outputs=[x[0] for x in paste_fields],
)
+ button.click(
+ fn=None,
+ _js=f"recalculate_prompts_{tabname}",
+ inputs=[],
+ outputs=[],
+ )
diff --git a/modules/images.py b/modules/images.py index 38404de3..7030aaaa 100644 --- a/modules/images.py +++ b/modules/images.py @@ -556,7 +556,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i elif image_to_save.mode == 'I;16':
image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")
- image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
+ image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)
if opts.enable_pnginfo and info is not None:
exif_bytes = piexif.dump({
@@ -573,6 +573,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i os.replace(temp_file_path, filename_without_extension + extension)
fullfn_without_extension, extension = os.path.splitext(params.filename)
+ if hasattr(os, 'statvfs'):
+ max_name_len = os.statvfs(path).f_namemax
+ fullfn_without_extension = fullfn_without_extension[:max_name_len - max(4, len(extension))]
+ params.filename = fullfn_without_extension + extension
+ fullfn = params.filename
_atomically_save_image(image, fullfn_without_extension, extension)
image.already_saved_as = fullfn
@@ -582,9 +587,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i ratio = image.width / image.height
if oversize and ratio > 1:
- image = image.resize((opts.target_side_length, image.height * opts.target_side_length // image.width), LANCZOS)
+ image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
elif oversize:
- image = image.resize((image.width * opts.target_side_length // image.height, opts.target_side_length), LANCZOS)
+ image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)
try:
_atomically_save_image(image, fullfn_without_extension, ".jpg")
@@ -640,6 +645,8 @@ Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]} def image_data(data):
+ import gradio as gr
+
try:
image = Image.open(io.BytesIO(data))
textinfo, _ = read_info_from_image(image)
@@ -655,7 +662,7 @@ def image_data(data): except Exception:
pass
- return '', None
+ return gr.update(), None
def flatten(img, bgcolor):
diff --git a/modules/mac_specific.py b/modules/mac_specific.py index ddcea53b..6fe8dea0 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -1,4 +1,5 @@ import torch +import platform from modules import paths from modules.sd_hijack_utils import CondFunc from packaging import version @@ -23,7 +24,7 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs): output_dtype = kwargs.get('dtype', input.dtype) if output_dtype == torch.int64: return cumsum_func(input.cpu(), *args, **kwargs).to(input.device) - elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16): + elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16): return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64) return cumsum_func(input, *args, **kwargs) @@ -32,6 +33,10 @@ if has_mps: # MPS fix for randn in torchsde CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps') + if platform.mac_ver()[0].startswith("13.2."): + # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124) + CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760) + if version.parse(torch.__version__) < version.parse("1.13"): # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working @@ -45,9 +50,10 @@ if has_mps: CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad) elif version.parse(torch.__version__) > version.parse("1.13.1"): cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0)) - cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0)) cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs) CondFunc('torch.cumsum', cumsum_fix_func, None) CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None) CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None) - + if version.parse(torch.__version__) == version.parse("2.0"): + # MPS workaround for https://github.com/pytorch/pytorch/issues/96113 + CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6) diff --git a/modules/memmon.py b/modules/memmon.py index a7060f58..4018edcc 100644 --- a/modules/memmon.py +++ b/modules/memmon.py @@ -23,12 +23,16 @@ class MemUsageMonitor(threading.Thread): self.data = defaultdict(int) try: - torch.cuda.mem_get_info() + self.cuda_mem_get_info() torch.cuda.memory_stats(self.device) except Exception as e: # AMD or whatever print(f"Warning: caught exception '{e}', memory monitor disabled") self.disabled = True + def cuda_mem_get_info(self): + index = self.device.index if self.device.index is not None else torch.cuda.current_device() + return torch.cuda.mem_get_info(index) + def run(self): if self.disabled: return @@ -43,10 +47,10 @@ class MemUsageMonitor(threading.Thread): self.run_flag.clear() continue - self.data["min_free"] = torch.cuda.mem_get_info()[0] + self.data["min_free"] = self.cuda_mem_get_info()[0] while self.run_flag.is_set(): - free, total = torch.cuda.mem_get_info() # calling with self.device errors, torch bug? + free, total = self.cuda_mem_get_info() self.data["min_free"] = min(self.data["min_free"], free) time.sleep(1 / self.opts.memmon_poll_rate) @@ -70,7 +74,7 @@ class MemUsageMonitor(threading.Thread): def read(self): if not self.disabled: - free, total = torch.cuda.mem_get_info() + free, total = self.cuda_mem_get_info() self.data["free"] = free self.data["total"] = total diff --git a/modules/modelloader.py b/modules/modelloader.py index fc3f6249..522affc6 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -4,9 +4,8 @@ import shutil import importlib from urllib.parse import urlparse -from basicsr.utils.download_util import load_file_from_url from modules import shared -from modules.upscaler import Upscaler +from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone from modules.paths import script_path, models_path @@ -59,6 +58,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None if model_url is not None and len(output) == 0: if download_name is not None: + from basicsr.utils.download_util import load_file_from_url dl = load_file_from_url(model_url, model_path, True, download_name) output.append(dl) else: @@ -169,4 +169,8 @@ def load_upscalers(): scaler = cls(commandline_options.get(cmd_name, None)) datas += scaler.scalers - shared.sd_upscalers = datas + shared.sd_upscalers = sorted( + datas, + # Special case for UpscalerNone keeps it at the beginning of the list. + key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else "" + ) diff --git a/modules/models/diffusion/uni_pc/__init__.py b/modules/models/diffusion/uni_pc/__init__.py new file mode 100644 index 00000000..e1265e3f --- /dev/null +++ b/modules/models/diffusion/uni_pc/__init__.py @@ -0,0 +1 @@ +from .sampler import UniPCSampler diff --git a/modules/models/diffusion/uni_pc/sampler.py b/modules/models/diffusion/uni_pc/sampler.py new file mode 100644 index 00000000..a241c8a7 --- /dev/null +++ b/modules/models/diffusion/uni_pc/sampler.py @@ -0,0 +1,100 @@ +"""SAMPLING ONLY.""" + +import torch + +from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC +from modules import shared, devices + + +class UniPCSampler(object): + def __init__(self, model, **kwargs): + super().__init__() + self.model = model + to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device) + self.before_sample = None + self.after_sample = None + self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod)) + + def register_buffer(self, name, attr): + if type(attr) == torch.Tensor: + if attr.device != devices.device: + attr = attr.to(devices.device) + setattr(self, name, attr) + + def set_hooks(self, before_sample, after_sample, after_update): + self.before_sample = before_sample + self.after_sample = after_sample + self.after_update = after_update + + @torch.no_grad() + def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + + elif isinstance(conditioning, list): + for ctmp in conditioning: + if ctmp.shape[0] != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + # print(f'Data shape for UniPC sampling is {size}') + + device = self.model.betas.device + if x_T is None: + img = torch.randn(size, device=device) + else: + img = x_T + + ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod) + + # SD 1.X is "noise", SD 2.X is "v" + model_type = "v" if self.model.parameterization == "v" else "noise" + + model_fn = model_wrapper( + lambda x, t, c: self.model.apply_model(x, t, c), + ns, + model_type=model_type, + guidance_type="classifier-free", + #condition=conditioning, + #unconditional_condition=unconditional_conditioning, + guidance_scale=unconditional_guidance_scale, + ) + + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update) + x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final) + + return x.to(device), None diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py new file mode 100644 index 00000000..eb5f4e76 --- /dev/null +++ b/modules/models/diffusion/uni_pc/uni_pc.py @@ -0,0 +1,857 @@ +import torch +import torch.nn.functional as F +import math +from tqdm.auto import trange + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + #condition=None, + #unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + if cond is None: + output = model(x, t_input, None, **model_kwargs) + else: + output = model(x, t_input, cond, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input, condition): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous, condition, unconditional_condition): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input, condition) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + if isinstance(condition, dict): + assert isinstance(unconditional_condition, dict) + c_in = dict() + for k in condition: + if isinstance(condition[k], list): + c_in[k] = [torch.cat([ + unconditional_condition[k][i], + condition[k][i]]) for i in range(len(condition[k]))] + else: + c_in[k] = torch.cat([ + unconditional_condition[k], + condition[k]]) + elif isinstance(condition, list): + c_in = list() + assert isinstance(unconditional_condition, list) + for i in range(len(condition)): + c_in.append(torch.cat([unconditional_condition[i], condition[i]])) + else: + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1', + condition=None, + unconditional_condition=None, + before_sample=None, + after_sample=None, + after_update=None + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model_fn_ = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + self.condition = condition + self.unconditional_condition = unconditional_condition + self.before_sample = before_sample + self.after_sample = after_sample + self.after_update = after_update + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model(self, x, t): + cond = self.condition + uncond = self.unconditional_condition + if self.before_sample is not None: + x, t, cond, uncond = self.before_sample(x, t, cond, uncond) + res = self.model_fn_(x, t, cond, uncond) + if self.after_sample is not None: + x, t, cond, uncond, res = self.after_sample(x, t, cond, uncond, res) + + if isinstance(res, tuple): + # (None, pred_x0) + res = res[1] + + return res + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + #print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + #print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + #print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, + ): + t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + if method == 'multistep': + assert steps >= order, "UniPC order must be < sampling steps" + timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}") + assert timesteps.shape[0] - 1 == steps + with torch.no_grad(): + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + # Init the first `order` values by lower order multistep DPM-Solver. + for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + if self.after_update is not None: + self.after_update(x, model_x) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + for step in trange(order, steps + 1): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + #print('this step order:', step_order) + if step == steps: + #print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + if self.after_update is not None: + self.after_update(x, model_x) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + else: + raise NotImplementedError() + if denoise_to_zero: + x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] diff --git a/modules/processing.py b/modules/processing.py index 2009d3bf..2e5a363f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -583,6 +583,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if state.job_count == -1:
state.job_count = p.n_iter
+ extra_network_data = None
for n in range(p.n_iter):
p.iteration = n
@@ -597,6 +598,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+ if p.scripts is not None:
+ p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+
if len(prompts) == 0:
break
@@ -685,6 +689,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image.info["parameters"] = text
output_images.append(image)
+ if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+ image_mask = p.mask_for_overlay.convert('RGB')
+ image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+
+ if opts.save_mask:
+ images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+
+ if opts.save_mask_composite:
+ images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+
+ if opts.return_mask:
+ output_images.append(image_mask)
+
+ if opts.return_mask_composite:
+ output_images.append(image_mask_composite)
+
del x_samples_ddim
devices.torch_gc()
@@ -709,7 +729,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
- if not p.disable_extra_networks:
+ if not p.disable_extra_networks and extra_network_data:
extra_networks.deactivate(p, extra_network_data)
devices.torch_gc()
@@ -888,7 +908,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): shared.state.nextjob()
- img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM' # PLMS does not support img2img so we just silently switch ot DDIM
+ img2img_sampler_name = self.sampler_name
+ if self.sampler_name in ['PLMS', 'UniPC']: # PLMS/UniPC do not support img2img so we just silently switch to DDIM
+ img2img_sampler_name = 'DDIM'
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index edd0e2a7..07911876 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -29,7 +29,7 @@ class ImageSaveParams: class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
+ def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
self.x = x
"""Latent image representation in the process of being denoised"""
@@ -44,6 +44,12 @@ class CFGDenoiserParams: self.total_sampling_steps = total_sampling_steps
"""Total number of sampling steps planned"""
+
+ self.text_cond = text_cond
+ """ Encoder hidden states of text conditioning from prompt"""
+
+ self.text_uncond = text_uncond
+ """ Encoder hidden states of text conditioning from negative prompt"""
class CFGDenoisedParams:
diff --git a/modules/scripts.py b/modules/scripts.py index 24056a12..d661be4f 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -33,6 +33,11 @@ class Script: parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
"""
+ paste_field_names = None
+ """if set in ui(), this is a list of names of infotext fields; the fields will be sent through the
+ various "Send to <X>" buttons when clicked
+ """
+
def title(self):
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@@ -80,6 +85,20 @@ class Script: pass
+ def before_process_batch(self, p, *args, **kwargs):
+ """
+ Called before extra networks are parsed from the prompt, so you can add
+ new extra network keywords to the prompt with this callback.
+
+ **kwargs will have those items:
+ - batch_number - index of current batch, from 0 to number of batches-1
+ - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+ - seeds - list of seeds for current batch
+ - subseeds - list of subseeds for current batch
+ """
+
+ pass
+
def process_batch(self, p, *args, **kwargs):
"""
Same as process(), but called for every batch.
@@ -220,7 +239,15 @@ def load_scripts(): elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
- for scriptfile in sorted(scripts_list):
+ def orderby(basedir):
+ # 1st webui, 2nd extensions-builtin, 3rd extensions
+ priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+ for key in priority:
+ if basedir.startswith(key):
+ return priority[key]
+ return 9999
+
+ for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
try:
if scriptfile.basedir != paths.script_path:
sys.path = [scriptfile.basedir] + sys.path
@@ -256,6 +283,7 @@ class ScriptRunner: self.alwayson_scripts = []
self.titles = []
self.infotext_fields = []
+ self.paste_field_names = []
def initialize_scripts(self, is_img2img):
from modules import scripts_auto_postprocessing
@@ -304,6 +332,9 @@ class ScriptRunner: if script.infotext_fields is not None:
self.infotext_fields += script.infotext_fields
+ if script.paste_field_names is not None:
+ self.paste_field_names += script.paste_field_names
+
inputs += controls
inputs_alwayson += [script.alwayson for _ in controls]
script.args_to = len(inputs)
@@ -388,6 +419,15 @@ class ScriptRunner: print(f"Error running process: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
+ def before_process_batch(self, p, **kwargs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.before_process_batch(p, *script_args, **kwargs)
+ except Exception:
+ print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
def process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
try:
@@ -481,6 +521,18 @@ def reload_scripts(): scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+def add_classes_to_gradio_component(comp):
+ """
+ this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+ """
+
+ comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+
+ if getattr(comp, 'multiselect', False):
+ comp.elem_classes.append('multiselect')
+
+
+
def IOComponent_init(self, *args, **kwargs):
if scripts_current is not None:
scripts_current.before_component(self, **kwargs)
@@ -489,6 +541,8 @@ def IOComponent_init(self, *args, **kwargs): res = original_IOComponent_init(self, *args, **kwargs)
+ add_classes_to_gradio_component(self)
+
script_callbacks.after_component_callback(self, **kwargs)
if scripts_current is not None:
diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py index ce0ebb61..b11568c0 100644 --- a/modules/scripts_postprocessing.py +++ b/modules/scripts_postprocessing.py @@ -109,7 +109,7 @@ class ScriptPostprocessingRunner: inputs = []
for script in self.scripts_in_preferred_order():
- with gr.Box() as group:
+ with gr.Row() as group:
self.create_script_ui(script, inputs)
script.group = group
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 79476783..f4bb0266 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -37,11 +37,23 @@ def apply_optimizations(): optimization_method = None
+ can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
+
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
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
optimization_method = 'xformers'
+ elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
+ print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
+ optimization_method = 'sdp-no-mem'
+ elif cmd_opts.opt_sdp_attention and can_use_sdp:
+ print("Applying scaled dot product cross attention optimization.")
+ ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
+ ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
+ optimization_method = 'sdp'
elif cmd_opts.opt_sub_quad_attention:
print("Applying sub-quadratic cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index c02d954c..372555ff 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -337,7 +337,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): dtype = q.dtype
if shared.opts.upcast_attn:
- q, k = q.float(), k.float()
+ q, k, v = q.float(), k.float(), v.float()
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
@@ -346,6 +346,52 @@ def xformers_attention_forward(self, x, context=None, mask=None): out = rearrange(out, 'b n h d -> b n (h d)', h=h)
return self.to_out(out)
+# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
+# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
+def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
+ batch_size, sequence_length, inner_dim = x.shape
+
+ if mask is not None:
+ mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
+ mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
+
+ h = self.heads
+ q_in = self.to_q(x)
+ context = default(context, x)
+
+ context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+ k_in = self.to_k(context_k)
+ v_in = self.to_v(context_v)
+
+ head_dim = inner_dim // h
+ q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+ v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+
+ del q_in, k_in, v_in
+
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k, v = q.float(), k.float(), v.float()
+
+ # the output of sdp = (batch, num_heads, seq_len, head_dim)
+ hidden_states = torch.nn.functional.scaled_dot_product_attention(
+ q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
+ )
+
+ hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
+ hidden_states = hidden_states.to(dtype)
+
+ # linear proj
+ hidden_states = self.to_out[0](hidden_states)
+ # dropout
+ hidden_states = self.to_out[1](hidden_states)
+ return hidden_states
+
+def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return scaled_dot_product_attention_forward(self, x, context, mask)
+
def cross_attention_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
@@ -427,6 +473,30 @@ def xformers_attnblock_forward(self, x): except NotImplementedError:
return cross_attention_attnblock_forward(self, x)
+def sdp_attnblock_forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+ b, c, h, w = q.shape
+ q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+ dtype = q.dtype
+ if shared.opts.upcast_attn:
+ q, k = q.float(), k.float()
+ q = q.contiguous()
+ k = k.contiguous()
+ v = v.contiguous()
+ out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
+ out = out.to(dtype)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+ out = self.proj_out(out)
+ return x + out
+
+def sdp_no_mem_attnblock_forward(self, x):
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+ return sdp_attnblock_forward(self, x)
+
def sub_quad_attnblock_forward(self, x):
h_ = x
h_ = self.norm(h_)
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py index 843ab66c..15858263 100644 --- a/modules/sd_hijack_unet.py +++ b/modules/sd_hijack_unet.py @@ -67,7 +67,7 @@ def hijack_ddpm_edit(): unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.1"):
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
diff --git a/modules/sd_models.py b/modules/sd_models.py index 127e9663..86218c08 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -105,9 +105,15 @@ def checkpoint_tiles(): def list_models():
checkpoints_list.clear()
checkpoint_alisases.clear()
- model_list = modelloader.load_models(model_path=model_path, model_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors", command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
cmd_ckpt = shared.cmd_opts.ckpt
+ if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
+ model_url = None
+ else:
+ model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+
+ model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
+
if os.path.exists(cmd_ckpt):
checkpoint_info = CheckpointInfo(cmd_ckpt)
checkpoint_info.register()
@@ -172,7 +178,7 @@ def select_checkpoint(): return checkpoint_info
-chckpoint_dict_replacements = {
+checkpoint_dict_replacements = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -180,7 +186,7 @@ chckpoint_dict_replacements = { def transform_checkpoint_dict_key(k):
- for text, replacement in chckpoint_dict_replacements.items():
+ for text, replacement in checkpoint_dict_replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
@@ -204,6 +210,30 @@ def get_state_dict_from_checkpoint(pl_sd): return pl_sd
+def read_metadata_from_safetensors(filename):
+ import json
+
+ with open(filename, mode="rb") as file:
+ metadata_len = file.read(8)
+ metadata_len = int.from_bytes(metadata_len, "little")
+ json_start = file.read(2)
+
+ assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
+ json_data = json_start + file.read(metadata_len-2)
+ json_obj = json.loads(json_data)
+
+ res = {}
+ for k, v in json_obj.get("__metadata__", {}).items():
+ res[k] = v
+ if isinstance(v, str) and v[0:1] == '{':
+ try:
+ res[k] = json.loads(v)
+ except Exception as e:
+ pass
+
+ return res
+
+
def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
_, extension = os.path.splitext(checkpoint_file)
if extension.lower() == ".safetensors":
@@ -464,7 +494,7 @@ def reload_model_weights(sd_model=None, info=None): if sd_model is None or checkpoint_config != sd_model.used_config:
del sd_model
checkpoints_loaded.clear()
- load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
+ load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return shared.sd_model
try:
@@ -487,3 +517,23 @@ def reload_model_weights(sd_model=None, info=None): print(f"Weights loaded in {timer.summary()}.")
return sd_model
+
+def unload_model_weights(sd_model=None, info=None):
+ from modules import lowvram, devices, sd_hijack
+ timer = Timer()
+
+ if shared.sd_model:
+
+ # shared.sd_model.cond_stage_model.to(devices.cpu)
+ # shared.sd_model.first_stage_model.to(devices.cpu)
+ shared.sd_model.to(devices.cpu)
+ sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+ shared.sd_model = None
+ sd_model = None
+ gc.collect()
+ devices.torch_gc()
+ torch.cuda.empty_cache()
+
+ print(f"Unloaded weights {timer.summary()}.")
+
+ return sd_model
\ No newline at end of file diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 28c2136f..ff361f22 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -32,7 +32,7 @@ def set_samplers(): global samplers, samplers_for_img2img
hidden = set(shared.opts.hide_samplers)
- hidden_img2img = set(shared.opts.hide_samplers + ['PLMS'])
+ hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])
samplers = [x for x in all_samplers if x.name not in hidden]
samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index d03131cd..083da18c 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -7,19 +7,27 @@ import torch from modules.shared import state
from modules import sd_samplers_common, prompt_parser, shared
+import modules.models.diffusion.uni_pc
samplers_data_compvis = [
sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+ sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
]
class VanillaStableDiffusionSampler:
def __init__(self, constructor, sd_model):
self.sampler = constructor(sd_model)
+ self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
self.is_plms = hasattr(self.sampler, 'p_sample_plms')
- self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
+ self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)
+ self.orig_p_sample_ddim = None
+ if self.is_plms:
+ self.orig_p_sample_ddim = self.sampler.p_sample_plms
+ elif self.is_ddim:
+ self.orig_p_sample_ddim = self.sampler.p_sample_ddim
self.mask = None
self.nmask = None
self.init_latent = None
@@ -45,6 +53,15 @@ class VanillaStableDiffusionSampler: return self.last_latent
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+ x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
+
+ res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+
+ x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
+
+ return res
+
+ def before_sample(self, x, ts, cond, unconditional_conditioning):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -76,7 +93,7 @@ class VanillaStableDiffusionSampler: 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
+ x = img_orig * self.mask + self.nmask * x
# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
# Note that they need to be lists because it just concatenates them later.
@@ -84,12 +101,13 @@ class VanillaStableDiffusionSampler: cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
- res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+ return x, ts, cond, unconditional_conditioning
+ def update_step(self, last_latent):
if self.mask is not None:
- self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
+ self.last_latent = self.init_latent * self.mask + self.nmask * last_latent
else:
- self.last_latent = res[1]
+ self.last_latent = last_latent
sd_samplers_common.store_latent(self.last_latent)
@@ -97,26 +115,51 @@ class VanillaStableDiffusionSampler: state.sampling_step = self.step
shared.total_tqdm.update()
- return res
+ def after_sample(self, x, ts, cond, uncond, res):
+ if not self.is_unipc:
+ self.update_step(res[1])
+
+ return x, ts, cond, uncond, res
+
+ def unipc_after_update(self, x, model_x):
+ self.update_step(x)
def initialize(self, p):
self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
if self.eta != 0.0:
p.extra_generation_params["Eta DDIM"] = self.eta
+ if self.is_unipc:
+ keys = [
+ ('UniPC variant', 'uni_pc_variant'),
+ ('UniPC skip type', 'uni_pc_skip_type'),
+ ('UniPC order', 'uni_pc_order'),
+ ('UniPC lower order final', 'uni_pc_lower_order_final'),
+ ]
+
+ for name, key in keys:
+ v = getattr(shared.opts, key)
+ if v != shared.opts.get_default(key):
+ p.extra_generation_params[name] = v
+
for fieldname in ['p_sample_ddim', 'p_sample_plms']:
if hasattr(self.sampler, fieldname):
setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+ if self.is_unipc:
+ self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
def adjust_steps_if_invalid(self, p, num_steps):
- if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
+ if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
+ if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:
+ num_steps = shared.opts.uni_pc_order
valid_step = 999 / (1000 // num_steps)
if valid_step == math.floor(valid_step):
return int(valid_step) + 1
-
+
return num_steps
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 528f513f..93f0e55a 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -101,11 +101,13 @@ class CFGDenoiser(torch.nn.Module): sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
sigma_in = denoiser_params.sigma
+ tensor = denoiser_params.text_cond
+ uncond = denoiser_params.text_uncond
if tensor.shape[1] == uncond.shape[1]:
if not is_edit_model:
diff --git a/modules/sd_vae_approx.py b/modules/sd_vae_approx.py index 0027343a..e2f00468 100644 --- a/modules/sd_vae_approx.py +++ b/modules/sd_vae_approx.py @@ -35,8 +35,11 @@ def model(): global sd_vae_approx_model
if sd_vae_approx_model is None:
+ model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
sd_vae_approx_model = VAEApprox()
- sd_vae_approx_model.load_state_dict(torch.load(os.path.join(paths.models_path, "VAE-approx", "model.pt"), map_location='cpu' if devices.device.type != 'cuda' else None))
+ if not os.path.exists(model_path):
+ model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
+ sd_vae_approx_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
sd_vae_approx_model.eval()
sd_vae_approx_model.to(devices.device, devices.dtype)
diff --git a/modules/shared.py b/modules/shared.py index 4edcb5ef..73ce77d4 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -69,6 +69,8 @@ parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size fo parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
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")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
@@ -81,6 +83,7 @@ parser.add_argument("--freeze-settings", action='store_true', help="disable edit parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
@@ -104,15 +107,20 @@ parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS o parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
+parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
+parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
script_loading.preload_extensions(extensions.extensions_dir, parser)
script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
-cmd_opts = parser.parse_args()
+if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
+ cmd_opts = parser.parse_args()
+else:
+ cmd_opts, _ = parser.parse_known_args()
restricted_opts = {
"samples_filename_pattern",
@@ -303,6 +311,7 @@ def list_samplers(): hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+tab_names = []
options_templates = {}
@@ -324,10 +333,14 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
+ "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
+ "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+ "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
"export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
+ "img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
@@ -438,13 +451,16 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (em)"),
- "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (em)"),
+ "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
+ "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
+ "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
"return_grid": OptionInfo(True, "Show grid in results for web"),
+ "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+ "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite 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(True, "Add model name to generation information"),
@@ -460,6 +476,7 @@ options_templates.update(options_section(('ui', "User interface"), { "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+ "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
@@ -485,6 +502,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+ 'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
+ 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
+ 'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
+ 'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
@@ -559,6 +580,15 @@ class Options: return True
+ def get_default(self, key):
+ """returns the default value for the key"""
+
+ data_label = self.data_labels.get(key)
+ if data_label is None:
+ return None
+
+ return data_label.default
+
def save(self, filename):
assert not cmd_opts.freeze_settings, "saving settings is disabled"
@@ -691,6 +721,7 @@ class TotalTQDM: def clear(self):
if self._tqdm is not None:
+ self._tqdm.refresh()
self._tqdm.close()
self._tqdm = None
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 1568b2b8..af9fbcf2 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -115,7 +115,7 @@ class PersonalizedBase(Dataset): weight /= weight.mean()
elif use_weight:
#If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
- weight = torch.ones([channels] + latent_size)
+ weight = torch.ones(latent_sample.shape)
else:
weight = None
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c63c7d1d..d2e62e58 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -152,7 +152,11 @@ class EmbeddingDatabase: name = data.get('name', name)
else:
data = extract_image_data_embed(embed_image)
- name = data.get('name', name)
+ if data:
+ name = data.get('name', name)
+ else:
+ # if data is None, means this is not an embeding, just a preview image
+ return
elif ext in ['.BIN', '.PT']:
data = torch.load(path, map_location="cpu")
elif ext in ['.SAFETENSORS']:
diff --git a/modules/timer.py b/modules/timer.py index 57a4f17a..ba92be33 100644 --- a/modules/timer.py +++ b/modules/timer.py @@ -33,3 +33,6 @@ class Timer: res += ")"
return res
+
+ def reset(self):
+ self.__init__()
diff --git a/modules/ui.py b/modules/ui.py index 0516c643..af8546c2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -20,7 +20,7 @@ from PIL import Image, PngImagePlugin from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
from modules.paths import script_path, data_path
from modules.shared import opts, cmd_opts, restricted_opts
@@ -89,7 +89,7 @@ paste_symbol = '\u2199\ufe0f' # ↙ refresh_symbol = '\U0001f504' # 🔄
save_style_symbol = '\U0001f4be' # 💾
apply_style_symbol = '\U0001f4cb' # 📋
-clear_prompt_symbol = '\U0001F5D1' # 🗑️
+clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
extra_networks_symbol = '\U0001F3B4' # 🎴
switch_values_symbol = '\U000021C5' # ⇅
@@ -179,14 +179,13 @@ def interrogate_deepbooru(image): def create_seed_inputs(target_interface):
- with FormRow(elem_id=target_interface + '_seed_row'):
+ with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
seed.style(container=False)
- random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
- reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+ random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
+ reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
- with gr.Group(elem_id=target_interface + '_subseed_show_box'):
- seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
# Components to show/hide based on the 'Extra' checkbox
seed_extras = []
@@ -195,8 +194,8 @@ def create_seed_inputs(target_interface): seed_extras.append(seed_extra_row_1)
subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
subseed.style(container=False)
- random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
- reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+ random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
+ reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
with FormRow(visible=False) as seed_extra_row_2:
@@ -291,19 +290,19 @@ def create_toprow(is_img2img): with gr.Row():
with gr.Column(scale=80):
with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
+ negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
button_interrogate = None
button_deepbooru = None
if is_img2img:
- with gr.Column(scale=1, elem_id="interrogate_col"):
+ with gr.Column(scale=1, elem_classes="interrogate-col"):
button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
- with gr.Row(elem_id=f"{id_part}_generate_box"):
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+ with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+ interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+ skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
skip.click(
@@ -325,9 +324,9 @@ def create_toprow(is_img2img): prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
- token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+ token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
- negative_token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_negative_token_counter")
+ negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
clear_prompt_button.click(
@@ -479,7 +478,9 @@ def create_ui(): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+ with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="txt2img_column_batch"):
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
@@ -492,7 +493,7 @@ def create_ui(): seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
elif category == "checkboxes":
- with FormRow(elem_id="txt2img_checkboxes", variant="compact"):
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
@@ -586,7 +587,7 @@ def create_ui(): txt2img_prompt.submit(**txt2img_args)
submit.click(**txt2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
+ res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
txt_prompt_img.change(
fn=modules.images.image_data,
@@ -757,7 +758,9 @@ def create_ui(): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+ with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="img2img_column_batch"):
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
@@ -774,7 +777,7 @@ def create_ui(): seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
elif category == "checkboxes":
- with FormRow(elem_id="img2img_checkboxes", variant="compact"):
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
@@ -904,7 +907,7 @@ def create_ui(): img2img_prompt.submit(**img2img_args)
submit.click(**img2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
+ res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
img2img_interrogate.click(
fn=lambda *args: process_interrogate(interrogate, *args),
@@ -939,7 +942,7 @@ def create_ui(): )
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
- negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+ negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])
ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
@@ -1491,11 +1494,33 @@ def create_ui(): request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ with gr.Row():
+ unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+ reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
with gr.TabItem("Licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
+
+ def unload_sd_weights():
+ modules.sd_models.unload_model_weights()
+
+ def reload_sd_weights():
+ modules.sd_models.reload_model_weights()
+
+ unload_sd_model.click(
+ fn=unload_sd_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ reload_sd_model.click(
+ fn=reload_sd_weights,
+ inputs=[],
+ outputs=[]
+ )
request_notifications.click(
fn=lambda: None,
@@ -1563,6 +1588,10 @@ def create_ui(): extensions_interface = ui_extensions.create_ui()
interfaces += [(extensions_interface, "Extensions", "extensions")]
+ shared.tab_names = []
+ for _interface, label, _ifid in interfaces:
+ shared.tab_names.append(label)
+
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"):
for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
@@ -1573,6 +1602,8 @@ def create_ui(): with gr.Tabs(elem_id="tabs") as tabs:
for interface, label, ifid in interfaces:
+ if label in shared.opts.hidden_tabs:
+ continue
with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
interface.render()
@@ -1592,11 +1623,13 @@ def create_ui(): for i, k, item in quicksettings_list:
component = component_dict[k]
+ info = opts.data_labels[k]
component.change(
fn=lambda value, k=k: run_settings_single(value, key=k),
inputs=[component],
outputs=[component, text_settings],
+ show_progress=info.refresh is not None,
)
text_settings.change(
@@ -1745,7 +1778,8 @@ def create_ui(): def reload_javascript():
- head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}?{os.path.getmtime("script.js")}"></script>\n'
+ script_js = os.path.join(script_path, "script.js")
+ head = f'<script type="text/javascript" src="file={os.path.abspath(script_js)}?{os.path.getmtime(script_js)}"></script>\n'
inline = f"{localization.localization_js(shared.opts.localization)};"
if cmd_opts.theme is not None:
@@ -1754,6 +1788,9 @@ def reload_javascript(): for script in modules.scripts.list_scripts("javascript", ".js"):
head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
+ for script in modules.scripts.list_scripts("javascript", ".mjs"):
+ head += f'<script type="module" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
+
head += f'<script type="text/javascript">{inline}</script>\n'
def template_response(*args, **kwargs):
diff --git a/modules/ui_common.py b/modules/ui_common.py index fd047f31..7b752b45 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -129,8 +129,8 @@ Requested path was: {f} generation_info = None
with gr.Column():
- with gr.Row(elem_id=f"image_buttons_{tabname}"):
- open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
+ with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+ open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
if tabname != "extras":
save = gr.Button('Save', elem_id=f'save_{tabname}')
@@ -160,6 +160,7 @@ Requested path was: {f} _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
inputs=[generation_info, html_info, html_info],
outputs=[html_info, html_info],
+ show_progress=False,
)
save.click(
@@ -198,9 +199,16 @@ Requested path was: {f} html_info = gr.HTML(elem_id=f'html_info_{tabname}')
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
+ paste_field_names = []
+ if tabname == "txt2img":
+ paste_field_names = modules.scripts.scripts_txt2img.paste_field_names
+ elif tabname == "img2img":
+ paste_field_names = modules.scripts.scripts_img2img.paste_field_names
+
for paste_tabname, paste_button in buttons.items():
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery
+ paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery,
+ paste_field_names=paste_field_names
))
return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
diff --git a/modules/ui_components.py b/modules/ui_components.py index 284ca0cf..2b1da2cb 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -1,55 +1,61 @@ import gradio as gr
-class ToolButton(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, fits inside gradio forms"""
+class FormComponent:
+ def get_expected_parent(self):
+ return gr.components.Form
- def __init__(self, **kwargs):
- super().__init__(variant="tool", **kwargs)
- def get_block_name(self):
- return "button"
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
+class ToolButton(FormComponent, gr.Button):
+ """Small button with single emoji as text, fits inside gradio forms"""
- def __init__(self, **kwargs):
- super().__init__(variant="tool-top", **kwargs)
+ def __init__(self, *args, **kwargs):
+ classes = kwargs.pop("elem_classes", [])
+ super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
def get_block_name(self):
return "button"
-class FormRow(gr.Row, gr.components.FormComponent):
+class FormRow(FormComponent, gr.Row):
"""Same as gr.Row but fits inside gradio forms"""
def get_block_name(self):
return "row"
-class FormGroup(gr.Group, gr.components.FormComponent):
+class FormColumn(FormComponent, gr.Column):
+ """Same as gr.Column but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
"""Same as gr.Row but fits inside gradio forms"""
def get_block_name(self):
return "group"
-class FormHTML(gr.HTML, gr.components.FormComponent):
+class FormHTML(FormComponent, gr.HTML):
"""Same as gr.HTML but fits inside gradio forms"""
def get_block_name(self):
return "html"
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
+class FormColorPicker(FormComponent, gr.ColorPicker):
"""Same as gr.ColorPicker but fits inside gradio forms"""
def get_block_name(self):
return "colorpicker"
-class DropdownMulti(gr.Dropdown):
+class DropdownMulti(FormComponent, gr.Dropdown):
"""Same as gr.Dropdown but always multiselect"""
def __init__(self, **kwargs):
super().__init__(multiselect=True, **kwargs)
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index bd4308ef..4a502974 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -1,6 +1,5 @@ import json
import os.path
-import shutil
import sys
import time
import traceback
@@ -141,22 +140,20 @@ def install_extension_from_url(dirname, url): try:
shutil.rmtree(tmpdir, True)
-
- repo = git.Repo.clone_from(url, tmpdir)
- repo.remote().fetch()
-
+ with git.Repo.clone_from(url, tmpdir) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
try:
os.rename(tmpdir, target_dir)
except OSError as err:
- # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
- # Shouldn't cause any new issues at least but we probably want to handle it there too.
if err.errno == errno.EXDEV:
# Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
# Since we can't use a rename, do the slower but more versitile shutil.move()
shutil.move(tmpdir, target_dir)
else:
# Something else, not enough free space, permissions, etc. rethrow it so that it gets handled.
- raise(err)
+ raise err
import launch
launch.run_extension_installer(target_dir)
@@ -244,7 +241,7 @@ def refresh_available_extensions_from_data(hide_tags, sort_column): hidden += 1
continue
- install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+ install_code = f"""<button onclick="install_extension_from_index(this, '{html.escape(url)}')" {"disabled=disabled" if existing else ""} class="lg secondary gradio-button custom-button">{"Install" if not existing else "Installed"}</button>"""
tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
@@ -304,7 +301,7 @@ def create_ui(): with gr.TabItem("Available"):
with gr.Row():
refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
- available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+ available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json", label="Extension index URL").style(container=False)
extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index 0c7ba173..08a69930 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -22,21 +22,37 @@ def register_page(page): allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
-def add_pages_to_demo(app):
- def fetch_file(filename: str = ""):
- from starlette.responses import FileResponse
+def fetch_file(filename: str = ""):
+ from starlette.responses import FileResponse
+
+ if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
+ raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+
+ ext = os.path.splitext(filename)[1].lower()
+ if ext not in (".png", ".jpg", ".webp"):
+ raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+
+ # would profit from returning 304
+ return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+
- if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
- raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+def get_metadata(page: str = "", item: str = ""):
+ from starlette.responses import JSONResponse
- ext = os.path.splitext(filename)[1].lower()
- if ext not in (".png", ".jpg"):
- raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg.")
+ page = next(iter([x for x in extra_pages if x.name == page]), None)
+ if page is None:
+ return JSONResponse({})
- # would profit from returning 304
- return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+ metadata = page.metadata.get(item)
+ if metadata is None:
+ return JSONResponse({})
+ return JSONResponse({"metadata": metadata})
+
+
+def add_pages_to_demo(app):
app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
+ app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])
class ExtraNetworksPage:
@@ -45,6 +61,7 @@ class ExtraNetworksPage: self.name = title.lower()
self.card_page = shared.html("extra-networks-card.html")
self.allow_negative_prompt = False
+ self.metadata = {}
def refresh(self):
pass
@@ -66,6 +83,8 @@ class ExtraNetworksPage: view = shared.opts.extra_networks_default_view
items_html = ''
+ self.metadata = {}
+
subdirs = {}
for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
@@ -86,12 +105,16 @@ class ExtraNetworksPage: subdirs = {"": 1, **subdirs}
subdirs_html = "".join([f"""
-<button class='gr-button gr-button-lg gr-button-secondary{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
+<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
{html.escape(subdir if subdir!="" else "all")}
</button>
""" for subdir in subdirs])
for item in self.list_items():
+ metadata = item.get("metadata")
+ if metadata:
+ self.metadata[item["name"]] = metadata
+
items_html += self.create_html_for_item(item, tabname)
if items_html == '':
@@ -124,9 +147,13 @@ class ExtraNetworksPage: if onclick is None:
onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
- height = f"height: {shared.opts.extra_networks_card_height}em;" if shared.opts.extra_networks_card_height else ''
- width = f"width: {shared.opts.extra_networks_card_width}em;" if shared.opts.extra_networks_card_width else ''
+ height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
+ width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
background_image = f"background-image: url(\"{html.escape(preview)}\");" if preview else ''
+ metadata_button = ""
+ metadata = item.get("metadata")
+ if metadata:
+ metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata({json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
args = {
"style": f"'{height}{width}{background_image}'",
@@ -134,13 +161,44 @@ class ExtraNetworksPage: "tabname": json.dumps(tabname),
"local_preview": json.dumps(item["local_preview"]),
"name": item["name"],
+ "description": (item.get("description") or ""),
"card_clicked": onclick,
"save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
"search_term": item.get("search_term", ""),
+ "metadata_button": metadata_button,
}
return self.card_page.format(**args)
+ def find_preview(self, path):
+ """
+ Find a preview PNG for a given path (without extension) and call link_preview on it.
+ """
+
+ preview_extensions = ["png", "jpg", "webp"]
+ if shared.opts.samples_format not in preview_extensions:
+ preview_extensions.append(shared.opts.samples_format)
+
+ potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
+
+ for file in potential_files:
+ if os.path.isfile(file):
+ return self.link_preview(file)
+
+ return None
+
+ def find_description(self, path):
+ """
+ Find and read a description file for a given path (without extension).
+ """
+ for file in [f"{path}.txt", f"{path}.description.txt"]:
+ try:
+ with open(file, "r", encoding="utf-8", errors="replace") as f:
+ return f.read()
+ except OSError:
+ pass
+ return None
+
def intialize():
extra_pages.clear()
@@ -182,12 +240,12 @@ def create_ui(container, button, tabname): with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
for page in ui.stored_extra_pages:
with gr.Tab(page.title):
+
page_elem = gr.HTML(page.create_html(ui.tabname))
ui.pages.append(page_elem)
filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
- button_close = gr.Button('Close', elem_id=tabname+"_extra_close")
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
@@ -198,7 +256,6 @@ def create_ui(container, button, tabname): state_visible = gr.State(value=False)
button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
- button_close.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
def refresh():
res = []
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index 04097a79..a17aa9c9 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -1,7 +1,6 @@ import html
import json
import os
-import urllib.parse
from modules import shared, ui_extra_networks, sd_models
@@ -17,21 +16,14 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): checkpoint: sd_models.CheckpointInfo
for name, checkpoint in sd_models.checkpoints_list.items():
path, ext = os.path.splitext(checkpoint.filename)
- previews = [path + ".png", path + ".preview.png"]
-
- preview = None
- for file in previews:
- if os.path.isfile(file):
- preview = self.link_preview(file)
- break
-
yield {
"name": checkpoint.name_for_extra,
"filename": path,
- "preview": preview,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
"search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
"onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"',
- "local_preview": path + ".png",
+ "local_preview": f"{path}.{shared.opts.samples_format}",
}
def allowed_directories_for_previews(self):
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index 57851088..6187e000 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -14,21 +14,15 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): def list_items(self):
for name, path in shared.hypernetworks.items():
path, ext = os.path.splitext(path)
- previews = [path + ".png", path + ".preview.png"]
-
- preview = None
- for file in previews:
- if os.path.isfile(file):
- preview = self.link_preview(file)
- break
yield {
"name": name,
"filename": path,
- "preview": preview,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
"search_term": self.search_terms_from_path(path),
"prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
- "local_preview": path + ".png",
+ "local_preview": f"{path}.preview.{shared.opts.samples_format}",
}
def allowed_directories_for_previews(self):
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index bb64eb81..6944d559 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -1,7 +1,7 @@ import json
import os
-from modules import ui_extra_networks, sd_hijack
+from modules import ui_extra_networks, sd_hijack, shared
class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
@@ -15,19 +15,14 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): def list_items(self):
for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values():
path, ext = os.path.splitext(embedding.filename)
- preview_file = path + ".preview.png"
-
- preview = None
- if os.path.isfile(preview_file):
- preview = self.link_preview(preview_file)
-
yield {
"name": embedding.name,
"filename": embedding.filename,
- "preview": preview,
+ "preview": self.find_preview(path),
+ "description": self.find_description(path),
"search_term": self.search_terms_from_path(embedding.filename),
"prompt": json.dumps(embedding.name),
- "local_preview": path + ".preview.png",
+ "local_preview": f"{path}.preview.{shared.opts.samples_format}",
}
def allowed_directories_for_previews(self):
diff --git a/requirements.txt b/requirements.txt index 6d53f089..3c40fd07 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ basicsr fonts
font-roboto
gfpgan
-gradio==3.16.2
+gradio==3.23
invisible-watermark
numpy
omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt index 331d0fe8..77e3da4e 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -3,7 +3,7 @@ transformers==4.25.1 accelerate==0.12.0
basicsr==1.4.2
gfpgan==1.3.8
-gradio==3.16.2
+gradio==3.23
numpy==1.23.3
Pillow==9.4.0
realesrgan==0.3.0
@@ -23,8 +23,8 @@ torchdiffeq==0.2.3 kornia==0.6.7
lark==1.1.2
inflection==0.5.1
-GitPython==3.1.27
+GitPython==3.1.30
torchsde==0.2.5
safetensors==0.2.7
httpcore<=0.15
-fastapi==0.90.1
+fastapi==0.94.0
@@ -1,7 +1,9 @@ function gradioApp() { const elems = document.getElementsByTagName('gradio-app') - const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot - return !!gradioShadowRoot ? gradioShadowRoot : document; + const elem = elems.length == 0 ? document : elems[0] + + elem.getElementById = function(id){ return document.getElementById(id) } + return elem.shadowRoot ? elem.shadowRoot : elem } function get_uiCurrentTab() { diff --git a/scripts/loopback.py b/scripts/loopback.py index 1dab9476..9c388aa8 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -1,13 +1,10 @@ -import numpy as np
-from tqdm import trange
+import math
-import modules.scripts as scripts
import gradio as gr
-
-from modules import processing, shared, sd_samplers, images
+import modules.scripts as scripts
+from modules import deepbooru, images, processing, shared
from modules.processing import Processed
-from modules.sd_samplers import samplers
-from modules.shared import opts, cmd_opts, state
+from modules.shared import opts, state
class Script(scripts.Script):
@@ -19,37 +16,68 @@ class Script(scripts.Script): def ui(self, is_img2img):
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
- denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
+ final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+ denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
+ append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
- return [loops, denoising_strength_change_factor]
+ return [loops, final_denoising_strength, denoising_curve, append_interrogation]
- def run(self, p, loops, denoising_strength_change_factor):
+ def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
processing.fix_seed(p)
batch_count = p.n_iter
p.extra_generation_params = {
- "Denoising strength change factor": denoising_strength_change_factor,
+ "Final denoising strength": final_denoising_strength,
+ "Denoising curve": denoising_curve
}
p.batch_size = 1
p.n_iter = 1
- output_images, info = None, None
+ info = None
initial_seed = None
initial_info = None
+ initial_denoising_strength = p.denoising_strength
grids = []
all_images = []
original_init_image = p.init_images
+ original_prompt = p.prompt
+ original_inpainting_fill = p.inpainting_fill
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
- for n in range(batch_count):
- history = []
+ def calculate_denoising_strength(loop):
+ strength = initial_denoising_strength
+
+ if loops == 1:
+ return strength
+
+ progress = loop / (loops - 1)
+ match denoising_curve:
+ case "Aggressive":
+ strength = math.sin((progress) * math.pi * 0.5)
+ case "Lazy":
+ strength = 1 - math.cos((progress) * math.pi * 0.5)
+
+ case _:
+ strength = progress
+
+ change = (final_denoising_strength - initial_denoising_strength) * strength
+ return initial_denoising_strength + change
+
+ history = []
+
+ for n in range(batch_count):
# Reset to original init image at the start of each batch
p.init_images = original_init_image
+ # Reset to original denoising strength
+ p.denoising_strength = initial_denoising_strength
+
+ last_image = None
+
for i in range(loops):
p.n_iter = 1
p.batch_size = 1
@@ -58,30 +86,57 @@ class Script(scripts.Script): if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
+ if append_interrogation != "None":
+ p.prompt = original_prompt + ", " if original_prompt != "" else ""
+ if append_interrogation == "CLIP":
+ p.prompt += shared.interrogator.interrogate(p.init_images[0])
+ elif append_interrogation == "DeepBooru":
+ p.prompt += deepbooru.model.tag(p.init_images[0])
+
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)
+ # Generation cancelled.
+ if state.interrupted:
+ break
+
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
- init_img = processed.images[0]
-
- p.init_images = [init_img]
p.seed = processed.seed + 1
- p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
- history.append(processed.images[0])
+ p.denoising_strength = calculate_denoising_strength(i + 1)
+
+ if state.skipped:
+ break
+ last_image = processed.images[0]
+ p.init_images = [last_image]
+ p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
+
+ if batch_count == 1:
+ history.append(last_image)
+ all_images.append(last_image)
+
+ if batch_count > 1 and not state.skipped and not state.interrupted:
+ history.append(last_image)
+ all_images.append(last_image)
+
+ p.inpainting_fill = original_inpainting_fill
+
+ if state.interrupted:
+ break
+
+ if len(history) > 1:
grid = images.image_grid(history, rows=1)
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
- grids.append(grid)
- all_images += history
-
- if opts.return_grid:
- all_images = grids + all_images
+ if opts.return_grid:
+ grids.append(grid)
+
+ all_images = grids + all_images
processed = Processed(p, all_images, initial_seed, initial_info)
diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py index 8842bd91..11eab31a 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -17,22 +17,24 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): def ui(self):
selected_tab = gr.State(value=0)
- with gr.Tabs(elem_id="extras_resize_mode"):
- with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
- upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
-
- with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
- with FormRow():
- upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
- upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
- upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
-
- with FormRow():
- extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-
- with FormRow():
- extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
- extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+ with gr.Column():
+ with FormRow():
+ with gr.Tabs(elem_id="extras_resize_mode"):
+ with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+
+ with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
+ with FormRow():
+ upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+ upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+
+ with FormRow():
+ extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+
+ with FormRow():
+ extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+ extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
diff --git a/scripts/prompt_matrix.py b/scripts/prompt_matrix.py index b1c486d4..e9b11517 100644 --- a/scripts/prompt_matrix.py +++ b/scripts/prompt_matrix.py @@ -100,7 +100,7 @@ class Script(scripts.Script): processed = process_images(p)
grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
- grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[1].height, prompt_matrix_parts, margin_size)
+ grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[0].height, prompt_matrix_parts, margin_size)
processed.images.insert(0, grid)
processed.index_of_first_image = 1
processed.infotexts.insert(0, processed.infotexts[0])
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 4afcbbfd..3551aca0 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -128,6 +128,24 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _): p.styles.extend(x.split(','))
+def apply_uni_pc_order(p, x, xs):
+ opts.data["uni_pc_order"] = min(x, p.steps - 1)
+
+
+def apply_face_restore(p, opt, x):
+ opt = opt.lower()
+ if opt == 'codeformer':
+ is_active = True
+ p.face_restoration_model = 'CodeFormer'
+ elif opt == 'gfpgan':
+ is_active = True
+ p.face_restoration_model = 'GFPGAN'
+ else:
+ is_active = opt in ('true', 'yes', 'y', '1')
+
+ p.restore_faces = is_active
+
+
def format_value_add_label(p, opt, x):
if type(x) == float:
x = round(x, 8)
@@ -205,6 +223,8 @@ axis_options = [ AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
+ AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
+ AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
]
@@ -213,46 +233,47 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
title_texts = [[images.GridAnnotation(z)] for z in z_labels]
- # Temporary list of all the images that are generated to be populated into the grid.
- # Will be filled with empty images for any individual step that fails to process properly
- image_cache = [None] * (len(xs) * len(ys) * len(zs))
+ list_size = (len(xs) * len(ys) * len(zs))
processed_result = None
- cell_mode = "P"
- cell_size = (1, 1)
- state.job_count = len(xs) * len(ys) * len(zs) * p.n_iter
+ state.job_count = list_size * p.n_iter
def process_cell(x, y, z, ix, iy, iz):
- nonlocal image_cache, processed_result, cell_mode, cell_size
+ nonlocal processed_result
def index(ix, iy, iz):
return ix + iy * len(xs) + iz * len(xs) * len(ys)
- state.job = f"{index(ix, iy, iz) + 1} out of {len(xs) * len(ys) * len(zs)}"
-
- processed: Processed = cell(x, y, z)
-
- try:
- # this dereference will throw an exception if the image was not processed
- # (this happens in cases such as if the user stops the process from the UI)
- processed_image = processed.images[0]
-
- if processed_result is None:
- # Use our first valid processed result as a template container to hold our full results
- processed_result = copy(processed)
- cell_mode = processed_image.mode
- cell_size = processed_image.size
- processed_result.images = [Image.new(cell_mode, cell_size)]
+ state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
+
+ processed: Processed = cell(x, y, z, ix, iy, iz)
+
+ if processed_result is None:
+ # Use our first processed result object as a template container to hold our full results
+ processed_result = copy(processed)
+ processed_result.images = [None] * list_size
+ processed_result.all_prompts = [None] * list_size
+ processed_result.all_seeds = [None] * list_size
+ processed_result.infotexts = [None] * list_size
+ processed_result.index_of_first_image = 1
+
+ idx = index(ix, iy, iz)
+ if processed.images:
+ # Non-empty list indicates some degree of success.
+ processed_result.images[idx] = processed.images[0]
+ processed_result.all_prompts[idx] = processed.prompt
+ processed_result.all_seeds[idx] = processed.seed
+ processed_result.infotexts[idx] = processed.infotexts[0]
+ else:
+ cell_mode = "P"
+ cell_size = (processed_result.width, processed_result.height)
+ if processed_result.images[0] is not None:
+ cell_mode = processed_result.images[0].mode
+ #This corrects size in case of batches:
+ cell_size = processed_result.images[0].size
+ processed_result.images[idx] = Image.new(cell_mode, cell_size)
- image_cache[index(ix, iy, iz)] = processed_image
- if include_lone_images:
- processed_result.images.append(processed_image)
- processed_result.all_prompts.append(processed.prompt)
- processed_result.all_seeds.append(processed.seed)
- processed_result.infotexts.append(processed.infotexts[0])
- except:
- image_cache[index(ix, iy, iz)] = Image.new(cell_mode, cell_size)
if first_axes_processed == 'x':
for ix, x in enumerate(xs):
@@ -286,36 +307,48 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend process_cell(x, y, z, ix, iy, iz)
if not processed_result:
+ # Should never happen, I've only seen it on one of four open tabs and it needed to refresh.
+ print("Unexpected error: Processing could not begin, you may need to refresh the tab or restart the service.")
+ return Processed(p, [])
+ elif not any(processed_result.images):
print("Unexpected error: draw_xyz_grid failed to return even a single processed image")
return Processed(p, [])
- sub_grids = [None] * len(zs)
- for i in range(len(zs)):
- start_index = i * len(xs) * len(ys)
+ z_count = len(zs)
+ sub_grids = [None] * z_count
+ for i in range(z_count):
+ start_index = (i * len(xs) * len(ys)) + i
end_index = start_index + len(xs) * len(ys)
- grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))
+ grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
if draw_legend:
- grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts, margin_size)
- sub_grids[i] = grid
- if include_sub_grids and len(zs) > 1:
- processed_result.images.insert(i+1, grid)
-
- sub_grid_size = sub_grids[0].size
- z_grid = images.image_grid(sub_grids, rows=1)
+ grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
+ processed_result.images.insert(i, grid)
+ processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
+ processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
+ processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
+
+ sub_grid_size = processed_result.images[0].size
+ z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
if draw_legend:
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
- processed_result.images[0] = z_grid
+ processed_result.images.insert(0, z_grid)
+ #TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
+ #processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
+ #processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
+ processed_result.infotexts.insert(0, processed_result.infotexts[0])
- return processed_result, sub_grids
+ return processed_result
class SharedSettingsStackHelper(object):
def __enter__(self):
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
self.vae = opts.sd_vae
+ self.uni_pc_order = opts.uni_pc_order
def __exit__(self, exc_type, exc_value, tb):
opts.data["sd_vae"] = self.vae
+ opts.data["uni_pc_order"] = self.uni_pc_order
modules.sd_models.reload_model_weights()
modules.sd_vae.reload_vae_weights()
@@ -415,7 +448,7 @@ class Script(scripts.Script): if opt.label == 'Nothing':
return [0]
- valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
+ valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
if opt.type == int:
valslist_ext = []
@@ -481,6 +514,10 @@ class Script(scripts.Script): z_opt = self.current_axis_options[z_type]
zs = process_axis(z_opt, z_values)
+ # this could be moved to common code, but unlikely to be ever triggered anywhere else
+ grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
+ assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
+
def fix_axis_seeds(axis_opt, axis_list):
if axis_opt.label in ['Seed', 'Var. seed']:
return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
@@ -521,8 +558,6 @@ class Script(scripts.Script): print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
shared.total_tqdm.updateTotal(total_steps)
- grid_infotext = [None]
-
state.xyz_plot_x = AxisInfo(x_opt, xs)
state.xyz_plot_y = AxisInfo(y_opt, ys)
state.xyz_plot_z = AxisInfo(z_opt, zs)
@@ -530,7 +565,7 @@ class Script(scripts.Script): # If one of the axes is very slow to change between (like SD model
# checkpoint), then make sure it is in the outer iteration of the nested
# `for` loop.
- first_axes_processed = 'x'
+ first_axes_processed = 'z'
second_axes_processed = 'y'
if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:
first_axes_processed = 'x'
@@ -551,7 +586,9 @@ class Script(scripts.Script): else:
second_axes_processed = 'y'
- def cell(x, y, z):
+ grid_infotext = [None] * (1 + len(zs))
+
+ def cell(x, y, z, ix, iy, iz):
if shared.state.interrupted:
return Processed(p, [], p.seed, "")
@@ -563,7 +600,9 @@ class Script(scripts.Script): res = process_images(pc)
- if grid_infotext[0] is None:
+ # Sets subgrid infotexts
+ subgrid_index = 1 + iz
+ if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
pc.extra_generation_params = copy(pc.extra_generation_params)
pc.extra_generation_params['Script'] = self.title()
@@ -579,6 +618,12 @@ class Script(scripts.Script): if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
+ grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+ # Sets main grid infotext
+ if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
+ pc.extra_generation_params = copy(pc.extra_generation_params)
+
if z_opt.label != 'Nothing':
pc.extra_generation_params["Z Type"] = z_opt.label
pc.extra_generation_params["Z Values"] = z_values
@@ -590,7 +635,7 @@ class Script(scripts.Script): return res
with SharedSettingsStackHelper():
- processed, sub_grids = draw_xyz_grid(
+ processed = draw_xyz_grid(
p,
xs=xs,
ys=ys,
@@ -607,11 +652,33 @@ class Script(scripts.Script): margin_size=margin_size
)
- if opts.grid_save and len(sub_grids) > 1:
- for sub_grid in sub_grids:
- images.save_image(sub_grid, p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+ if not processed.images:
+ # It broke, no further handling needed.
+ return processed
+
+ z_count = len(zs)
+
+ # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+ processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
+
+ if not include_lone_images:
+ # Don't need sub-images anymore, drop from list:
+ processed.images = processed.images[:z_count+1]
if opts.grid_save:
- images.save_image(processed.images[0], p.outpath_grids, "xyz_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+ # Auto-save main and sub-grids:
+ grid_count = z_count + 1 if z_count > 1 else 1
+ for g in range(grid_count):
+ #TODO: See previous comment about intentional data misalignment.
+ adj_g = g-1 if g > 0 else g
+ images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
+
+ if not include_sub_grids:
+ # Done with sub-grids, drop all related information:
+ for sg in range(z_count):
+ del processed.images[1]
+ del processed.all_prompts[1]
+ del processed.all_seeds[1]
+ del processed.infotexts[1]
return processed
@@ -1,270 +1,313 @@ -.container {
- max-width: 100%;
-}
-.token-counter{
- position: absolute;
- display: inline-block;
- right: 2em;
- min-width: 0 !important;
- width: auto;
- z-index: 100;
-}
+/* general gradio fixes */
-.token-counter.error span{
- box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075);
- border: 2px solid rgba(255,0,0,0.4) !important;
+:root, .dark{
+ --checkbox-label-gap: 0.25em 0.1em;
+ --section-header-text-size: 12pt;
+ --block-background-fill: transparent;
}
-.token-counter div{
- display: inline;
+.block.padded{
+ padding: 0 !important;
}
-.token-counter span{
- padding: 0.1em 0.75em;
+div.gradio-container{
+ max-width: unset !important;
}
-#sh{
- min-width: 2em;
- min-height: 2em;
- max-width: 2em;
- max-height: 2em;
- flex-grow: 0;
- padding-left: 0.25em;
- padding-right: 0.25em;
- margin: 0.1em 0;
- opacity: 0%;
- cursor: default;
+.hidden{
+ display: none;
}
-.output-html p {margin: 0 0.5em;}
-
-.row > *,
-.row > .gr-form > * {
- min-width: min(120px, 100%);
- flex: 1 1 0%;
+.compact{
+ background: transparent !important;
+ padding: 0 !important;
}
-.performance {
- font-size: 0.85em;
- color: #444;
+div.form{
+ border-width: 0;
+ box-shadow: none;
+ background: transparent;
+ overflow: visible;
+ gap: 0.5em;
}
-.performance p{
- display: inline-block;
+.block.gradio-dropdown,
+.block.gradio-slider,
+.block.gradio-checkbox,
+.block.gradio-textbox,
+.block.gradio-radio,
+.block.gradio-checkboxgroup,
+.block.gradio-number,
+.block.gradio-colorpicker
+{
+ border-width: 0 !important;
+ box-shadow: none !important;
}
-.performance .time {
- margin-right: 0;
+.gap.compact{
+ padding: 0;
+ gap: 0.2em 0;
}
-.performance .vram {
+div.compact{
+ gap: 1em;
}
-#txt2img_generate, #img2img_generate {
- min-height: 4.5em;
+.gradio-dropdown ul.options{
+ z-index: 3000;
}
-@media screen and (min-width: 2500px) {
- #txt2img_gallery, #img2img_gallery {
- min-height: 768px;
- }
+.gradio-dropdown label span:not(.has-info),
+.gradio-textbox label span:not(.has-info),
+.gradio-number label span:not(.has-info)
+{
+ margin-bottom: 0;
}
-#txt2img_gallery img, #img2img_gallery img{
- object-fit: scale-down;
+.gradio-dropdown div.wrap.wrap.wrap.wrap{
+ box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
}
-#txt2img_actions_column, #img2img_actions_column {
- margin: 0.35rem 0.75rem 0.35rem 0;
+
+.gradio-dropdown .wrap-inner.wrap-inner.wrap-inner{
+ flex-wrap: unset;
}
-#script_list {
- padding: .625rem .75rem 0 .625rem;
+
+.gradio-dropdown .single-select{
+ white-space: nowrap;
+ overflow: hidden;
}
-.justify-center.overflow-x-scroll {
- justify-content: left;
+
+.gradio-dropdown .token-remove.remove-all.remove-all{
+ display: none;
}
-.justify-center.overflow-x-scroll button:first-of-type {
- margin-left: auto;
+.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{
+ display: flex;
}
-.justify-center.overflow-x-scroll button:last-of-type {
- margin-right: auto;
+.gradio-slider input[type="number"]{
+ width: 6em;
}
-[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
- min-width: 2.3em;
- height: 2.5em;
- flex-grow: 0;
- padding-left: 0.25em;
- padding-right: 0.25em;
+.block.gradio-checkbox {
+ margin: 0.75em 1.5em 0 0;
}
-#hidden_element{
- display: none;
+.gradio-html div.wrap{
+ height: 100%;
+}
+div.gradio-html.min{
+ min-height: 0;
}
-[id$=_seed_row], [id$=_subseed_row]{
- gap: 0.5rem;
- padding: 0.6em;
+.block.gradio-gallery{
+ background: var(--input-background-fill);
}
-[id$=_subseed_show_box]{
- min-width: auto;
- flex-grow: 0;
+.gradio-container .prose a, .gradio-container .prose a:visited{
+ color: unset;
+ text-decoration: none;
}
-[id$=_subseed_show_box] > div{
- border: 0;
- height: 100%;
+
+
+/* general styled components */
+
+.gradio-button.tool{
+ max-width: 2.2em;
+ min-width: 2.2em !important;
+ height: 2.4em;
+ align-self: end;
+ line-height: 1em;
+ border-radius: 0.5em;
}
-[id$=_subseed_show]{
+.checkboxes-row{
+ margin-bottom: 0.5em;
+ margin-left: 0em;
+}
+.checkboxes-row > div{
+ flex: 0;
+ white-space: nowrap;
min-width: auto;
- flex-grow: 0;
- padding: 0;
}
-[id$=_subseed_show] label{
- height: 100%;
+button.custom-button{
+ border-radius: var(--button-large-radius);
+ padding: var(--button-large-padding);
+ font-weight: var(--button-large-text-weight);
+ border: var(--button-border-width) solid var(--button-secondary-border-color);
+ background: var(--button-secondary-background-fill);
+ color: var(--button-secondary-text-color);
+ font-size: var(--button-large-text-size);
+ display: inline-flex;
+ justify-content: center;
+ align-items: center;
+ transition: var(--button-transition);
+ box-shadow: var(--button-shadow);
+ text-align: center;
}
-#txt2img_actions_column, #img2img_actions_column{
- gap: 0;
- margin-right: .75rem;
-}
-#txt2img_tools, #img2img_tools{
- gap: 0.4em;
-}
+/* txt2img/img2img specific */
-#interrogate_col{
+.block.token-counter{
+ position: absolute;
+ display: inline-block;
+ right: 1em;
min-width: 0 !important;
- max-width: 8em !important;
- margin-right: 1em;
- gap: 0;
+ width: auto;
+ z-index: 100;
+ top: -0.75em;
}
-#interrogate, #deepbooru{
- margin: 0em 0.25em 0.5em 0.25em;
- min-width: 8em;
- max-width: 8em;
+
+.block.token-counter span{
+ background: var(--input-background-fill) !important;
+ box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075);
+ border: 2px solid rgba(192,192,192,0.4) !important;
+ border-radius: 0.4em;
}
-#style_pos_col, #style_neg_col{
- min-width: 8em !important;
+.block.token-counter.error span{
+ box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075);
+ border: 2px solid rgba(255,0,0,0.4) !important;
}
-#txt2img_styles_row, #img2img_styles_row{
- gap: 0.25em;
- margin-top: 0.3em;
+.block.token-counter div{
+ display: inline;
}
-#txt2img_styles_row > button, #img2img_styles_row > button{
- margin: 0;
+.block.token-counter span{
+ padding: 0.1em 0.75em;
}
-#txt2img_styles, #img2img_styles{
- padding: 0;
+[id$=_subseed_show]{
+ min-width: auto !important;
+ flex-grow: 0 !important;
+ display: flex;
+}
+
+[id$=_subseed_show] label{
+ margin-bottom: 0.5em;
+ align-self: end;
}
-#txt2img_styles > label > div, #img2img_styles > label > div{
- min-height: 3.2em;
+.performance {
+ font-size: 0.85em;
+ color: #444;
}
-ul.list-none{
- max-height: 35em;
- z-index: 2000;
+.performance p{
+ display: inline-block;
}
-.gr-form{
- background: transparent;
+.performance .time {
+ margin-right: 0;
}
-.my-4{
- margin-top: 0;
- margin-bottom: 0;
+.performance .vram {
}
-#resize_mode{
- flex: 1.5;
+#txt2img_generate, #img2img_generate {
+ min-height: 4.5em;
}
-button{
- align-self: stretch !important;
+@media screen and (min-width: 2500px) {
+ #txt2img_gallery, #img2img_gallery {
+ min-height: 768px;
+ }
}
-.overflow-hidden, .gr-panel{
- overflow: visible !important;
+#txt2img_gallery img, #img2img_gallery img{
+ object-fit: scale-down;
+}
+#txt2img_actions_column, #img2img_actions_column {
+ gap: 0.5em;
+}
+#txt2img_tools, #img2img_tools{
+ gap: 0.4em;
}
-#x_type, #y_type{
- max-width: 10em;
+.interrogate-col{
+ min-width: 0 !important;
+ max-width: fit-content;
+ gap: 0.5em;
+}
+.interrogate-col > button{
+ flex: 1;
}
-#txt2img_preview, #img2img_preview, #ti_preview{
+.generate-box{
+ position: relative;
+}
+.gradio-button.generate-box-skip, .gradio-button.generate-box-interrupt{
position: absolute;
- width: 320px;
+ width: 50%;
+ height: 100%;
+ display: none;
+ background: #b4c0cc;
+}
+.gradio-button.generate-box-skip:hover, .gradio-button.generate-box-interrupt:hover{
+ background: #c2cfdb;
+}
+.gradio-button.generate-box-interrupt{
left: 0;
+ border-radius: 0.5rem 0 0 0.5rem;
+}
+.gradio-button.generate-box-skip{
right: 0;
- margin-left: auto;
- margin-right: auto;
- margin-top: 34px;
- z-index: 100;
- border: none;
- border-top-left-radius: 0;
- border-top-right-radius: 0;
+ border-radius: 0 0.5rem 0.5rem 0;
}
-@media screen and (min-width: 768px) {
- #txt2img_preview, #img2img_preview, #ti_preview {
- position: absolute;
- }
+#txtimg_hr_finalres{
+ min-height: 0 !important;
+ padding: .625rem .75rem;
+ margin-left: -0.75em
}
-@media screen and (max-width: 767px) {
- #txt2img_preview, #img2img_preview, #ti_preview {
- position: relative;
- }
+#txtimg_hr_finalres .resolution{
+ font-weight: bold;
}
-#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
- display: none;
+.inactive{
+ opacity: 0.5;
}
-fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{
- position: absolute;
- top: -0.7em;
- line-height: 1.2em;
- padding: 0;
- margin: 0 0.5em;
-
- background-color: white;
- box-shadow: 6px 0 6px 0px white, -6px 0 6px 0px white;
+[id$=_column_batch]{
+ min-width: min(13.5em, 100%) !important;
+}
- z-index: 300;
+div.dimensions-tools{
+ min-width: 0 !important;
+ max-width: fit-content;
+ flex-direction: row;
+ align-content: center;
}
-.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
- background-color: rgb(31, 41, 55);
- box-shadow: none;
- border: 1px solid rgba(128, 128, 128, 0.1);
- border-radius: 6px;
- padding: 0.1em 0.5em;
+#mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
+ height: 480px !important;
+ max-height: 480px !important;
+ min-height: 480px !important;
}
-#txt2img_column_batch, #img2img_column_batch{
- min-width: min(13.5em, 100%) !important;
+.image-buttons button{
+ min-width: auto;
}
-#settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
- position: relative;
- border: none;
- margin-right: 8em;
+
+/* settings */
+#quicksettings {
+ width: fit-content;
}
-#settings .gr-panel div.flex-col div.justify-between div{
- position: relative;
- z-index: 200;
+#quicksettings > div, #quicksettings > fieldset{
+ max-width: 24em;
+ min-width: 24em;
+ padding: 0;
+ border: none;
+ box-shadow: none;
+ background: none;
}
#settings{
@@ -276,17 +319,18 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s margin-left: 10em;
}
-#settings > div.flex-wrap{
+#settings > div.tab-nav{
float: left;
display: block;
margin-left: 0;
width: 10em;
}
-#settings > div.flex-wrap button{
+#settings > div.tab-nav button{
display: block;
border: none;
text-align: left;
+ white-space: initial;
}
#settings_result{
@@ -294,29 +338,8 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s margin: 0 1.2em;
}
-input[type="range"]{
- margin: 0.5em 0 -0.3em 0;
-}
-
-#mask_bug_info {
- text-align: center;
- display: block;
- margin-top: -0.75em;
- margin-bottom: -0.75em;
-}
-
-#txt2img_negative_prompt, #img2img_negative_prompt{
-}
-
-/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */
-.transition.opacity-20 {
- opacity: 1 !important;
-}
-
-/* more gradio's garbage cleanup */
-.min-h-\[4rem\] { min-height: unset !important; }
-.min-h-\[6rem\] { min-height: unset !important; }
+/* live preview */
.progressDiv{
position: relative;
height: 20px;
@@ -362,89 +385,96 @@ input[type="range"]{ height: 100%;
}
-#lightboxModal{
- display: none;
- position: fixed;
- z-index: 1001;
- padding-top: 100px;
- left: 0;
- top: 0;
- width: 100%;
- height: 100%;
- overflow: auto;
- background-color: rgba(20, 20, 20, 0.95);
- user-select: none;
- -webkit-user-select: none;
+/* fullscreen popup (ie in Lora's (i) button) */
+
+.popup-metadata{
+ color: black;
+ background: white;
+ display: inline-block;
+ padding: 1em;
+ white-space: pre-wrap;
}
-.modalControls {
- display: grid;
- grid-template-columns: 32px 32px 32px 1fr 32px;
- grid-template-areas: "zoom tile save space close";
- position: absolute;
- top: 0;
+.global-popup{
+ display: flex;
+ position: fixed;
+ z-index: 1001;
left: 0;
- right: 0;
- padding: 16px;
- gap: 16px;
- background-color: rgba(0,0,0,0.2);
+ top: 0;
+ width: 100%;
+ height: 100%;
+ overflow: auto;
+ background-color: rgba(20, 20, 20, 0.95);
}
-.modalClose {
- grid-area: close;
-}
-.modalZoom {
- grid-area: zoom;
+.global-popup-close:before {
+ content: "×";
}
-.modalSave {
- grid-area: save;
+.global-popup-close{
+ position: fixed;
+ right: 0.25em;
+ top: 0;
+ cursor: pointer;
+ color: white;
+ font-size: 32pt;
}
-.modalTileImage {
- grid-area: tile;
+.global-popup-inner{
+ display: inline-block;
+ margin: auto;
+ padding: 2em;
}
-.modalClose,
-.modalZoom,
-.modalTileImage {
- color: white;
- font-size: 35px;
- font-weight: bold;
- cursor: pointer;
+/* fullpage image viewer */
+
+#lightboxModal{
+ display: none;
+ position: fixed;
+ z-index: 1001;
+ left: 0;
+ top: 0;
+ width: 100%;
+ height: 100%;
+ overflow: auto;
+ background-color: rgba(20, 20, 20, 0.95);
+ user-select: none;
+ -webkit-user-select: none;
+ flex-direction: column;
}
-.modalSave {
+.modalControls {
+ display: flex;
+ gap: 1em;
+ padding: 1em;
+ background-color: rgba(0,0,0,0.2);
+}
+.modalClose {
+ margin-left: auto;
+}
+.modalControls span{
color: white;
- font-size: 28px;
- margin-top: 8px;
+ font-size: 35px;
font-weight: bold;
cursor: pointer;
+ width: 1em;
}
-.modalClose:hover,
-.modalClose:focus,
-.modalSave:hover,
-.modalSave:focus,
-.modalZoom:hover,
-.modalZoom:focus {
- color: #999;
- text-decoration: none;
- cursor: pointer;
+.modalControls span:hover, .modalControls span:focus{
+ color: #999;
+ text-decoration: none;
}
-#modalImage {
+#lightboxModal > img {
display: block;
- margin-left: auto;
- margin-right: auto;
- margin-top: auto;
+ margin: auto;
width: auto;
}
-.modalImageFullscreen {
+#lightboxModal > img.modalImageFullscreen{
object-fit: contain;
- height: 90%;
+ height: 100%;
}
.modalPrev,
@@ -474,45 +504,7 @@ input[type="range"]{ background-color: rgba(0, 0, 0, 0.8);
}
-#imageARPreview{
- position:absolute;
- top:0px;
- left:0px;
- border:2px solid red;
- background:rgba(255, 0, 0, 0.3);
- z-index: 900;
- pointer-events:none;
- display:none
-}
-
-#txt2img_generate_box, #img2img_generate_box{
- position: relative;
-}
-
-#txt2img_interrupt, #img2img_interrupt, #txt2img_skip, #img2img_skip{
- position: absolute;
- width: 50%;
- height: 100%;
- background: #b4c0cc;
- display: none;
-}
-
-#txt2img_interrupt, #img2img_interrupt{
- left: 0;
- border-radius: 0.5rem 0 0 0.5rem;
-}
-#txt2img_skip, #img2img_skip{
- right: 0;
- border-radius: 0 0.5rem 0.5rem 0;
-}
-
-.red {
- color: red;
-}
-
-.gallery-item {
- --tw-bg-opacity: 0 !important;
-}
+/* context menu (ie for the generate button) */
#context-menu{
z-index:9999;
@@ -541,61 +533,8 @@ input[type="range"]{ background: #a55000;
}
-#quicksettings {
- width: fit-content;
-}
-#quicksettings > div, #quicksettings > fieldset{
- max-width: 24em;
- min-width: 24em;
- padding: 0;
- border: none;
- box-shadow: none;
- background: none;
- margin-right: 10px;
-}
-
-#quicksettings > div > div > div > label > span {
- position: relative;
- margin-right: 9em;
- margin-bottom: -1em;
-}
-
-canvas[key="mask"] {
- z-index: 12 !important;
- filter: invert();
- mix-blend-mode: multiply;
- pointer-events: none;
-}
-
-
-/* gradio 3.4.1 stuff for editable scrollbar values */
-.gr-box > div > div > input.gr-text-input{
- position: absolute;
- right: 0.5em;
- top: -0.6em;
- z-index: 400;
- width: 6em;
-}
-#quicksettings .gr-box > div > div > input.gr-text-input {
- top: -1.12em;
-}
-
-.row.gr-compact{
- overflow: visible;
-}
-
-#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
-#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img,
-#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img,
-#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img
-{
- height: 480px !important;
- max-height: 480px !important;
- min-height: 480px !important;
-}
-
-/* Extensions */
+/* extensions */
#tab_extensions table{
border-collapse: collapse;
@@ -608,6 +547,7 @@ canvas[key="mask"] { #tab_extensions table input[type="checkbox"]{
margin-right: 0.5em;
+ appearance: checkbox;
}
#tab_extensions button{
@@ -632,74 +572,7 @@ canvas[key="mask"] { font-size: 90%;
}
-#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{
- min-width: auto;
- padding-left: 0.5em;
- padding-right: 0.5em;
-}
-
-.gr-form{
- background-color: white;
-}
-
-.dark .gr-form{
- background-color: rgb(31 41 55 / var(--tw-bg-opacity));
-}
-
-.gr-button-tool, .gr-button-tool-top{
- max-width: 2.5em;
- min-width: 2.5em !important;
- height: 2.4em;
-}
-
-.gr-button-tool{
- margin: 0.6em 0em 0.55em 0;
-}
-
-.gr-button-tool-top, #settings .gr-button-tool{
- margin: 1.6em 0.7em 0.55em 0;
-}
-
-
-#modelmerger_results_container{
- margin-top: 1em;
- overflow: visible;
-}
-
-#modelmerger_models{
- gap: 0;
-}
-
-
-#quicksettings .gr-button-tool{
- margin: 0;
- border-color: unset;
- background-color: unset;
-}
-
-#modelmerger_interp_description>p {
- margin: 0!important;
- text-align: center;
-}
-#modelmerger_interp_description {
- margin: 0.35rem 0.75rem 1.23rem;
-}
-#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {
- padding-top: 0.9em;
- padding-bottom: 0.9em;
-}
-#txt2img_settings {
- padding-top: 1.16em;
- padding-bottom: 0.9em;
-}
-#img2img_settings {
- padding-bottom: 0.9em;
-}
-
-#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{
- border: none;
- padding-bottom: 0.5em;
-}
+/* replace original footer with ours */
footer {
display: none !important;
@@ -718,90 +591,7 @@ footer { opacity: 0.85;
}
-#txtimg_hr_finalres{
- min-height: 0 !important;
- padding: .625rem .75rem;
- margin-left: -0.75em
-
-}
-
-#txtimg_hr_finalres .resolution{
- font-weight: bold;
-}
-
-#txt2img_checkboxes, #img2img_checkboxes{
- margin-bottom: 0.5em;
- margin-left: 0em;
-}
-#txt2img_checkboxes > div, #img2img_checkboxes > div{
- flex: 0;
- white-space: nowrap;
- min-width: auto;
-}
-
-#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{
- margin-left: 0em;
-}
-
-#axis_options {
- margin-left: 0em;
-}
-
-.inactive{
- opacity: 0.5;
-}
-
-[id*='_prompt_container']{
- gap: 0;
-}
-
-[id*='_prompt_container'] > div{
- margin: -0.4em 0 0 0;
-}
-
-.gr-compact {
- border: none;
-}
-
-.dark .gr-compact{
- background-color: rgb(31 41 55 / var(--tw-bg-opacity));
- margin-left: 0;
-}
-
-.gr-compact{
- overflow: visible;
-}
-
-.gr-compact > *{
-}
-
-.gr-compact .gr-block, .gr-compact .gr-form{
- border: none;
- box-shadow: none;
-}
-
-.gr-compact .gr-box{
- border-radius: .5rem !important;
- border-width: 1px !important;
-}
-
-#mode_img2img > div > div{
- gap: 0 !important;
-}
-
-[id*='img2img_copy_to_'] {
- border: none;
-}
-
-[id*='img2img_copy_to_'] > button {
-}
-
-[id*='img2img_label_copy_to_'] {
- font-size: 1.0em;
- font-weight: bold;
- text-align: center;
- line-height: 2.4em;
-}
+/* extra networks UI */
.extra-networks > div > [id *= '_extra_']{
margin: 0.3em;
@@ -814,12 +604,12 @@ footer { .extra-network-subdirs button{
margin: 0 0.15em;
}
-
-#txt2img_extra_networks .search, #img2img_extra_networks .search{
+.extra-networks .tab-nav .search{
display: inline-block;
max-width: 16em;
margin: 0.3em;
align-self: center;
+ width: 16em;
}
#txt2img_extra_view, #img2img_extra_view {
@@ -839,6 +629,28 @@ footer { margin-left: 0.5em;
}
+
+.extra-network-cards .card .metadata-button:before, .extra-network-thumbs .card .metadata-button:before{
+ content: "🛈";
+}
+.extra-network-cards .card .metadata-button, .extra-network-thumbs .card .metadata-button{
+ display: none;
+ position: absolute;
+ right: 0;
+ color: white;
+ text-shadow: 2px 2px 3px black;
+ padding: 0.25em;
+ font-size: 22pt;
+ width: 1.5em;
+}
+.extra-network-cards .card:hover .metadata-button, .extra-network-thumbs .card:hover .metadata-button{
+ display: inline-block;
+}
+.extra-network-cards .card .metadata-button:hover, .extra-network-thumbs .card .metadata-button:hover{
+ color: red;
+}
+
+
.extra-network-thumbs {
display: flex;
flex-flow: row wrap;
@@ -856,7 +668,7 @@ footer { }
.extra-network-thumbs .card:hover .additional a {
- display: block;
+ display: inline-block;
}
.extra-network-thumbs .actions .additional a {
@@ -923,12 +735,15 @@ footer { left: 0;
right: 0;
padding: 0.5em;
- color: white;
background: rgba(0,0,0,0.5);
box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5);
text-shadow: 0 0 0.2em black;
}
+.extra-network-cards .card .actions *{
+ color: white;
+}
+
.extra-network-cards .card .actions:hover{
box-shadow: 0 0 0.75em 0.75em rgba(0,0,0,0.5) !important;
}
@@ -939,6 +754,17 @@ footer { line-break: anywhere;
}
+.extra-network-cards .card .actions .description {
+ display: block;
+ max-height: 3em;
+ white-space: pre-wrap;
+ line-height: 1.1;
+}
+
+.extra-network-cards .card .actions .description:hover {
+ max-height: none;
+}
+
.extra-network-cards .card .actions:hover .additional{
display: block;
}
@@ -955,7 +781,3 @@ footer { .extra-network-cards .card ul a:hover{
color: red;
}
-
-[id*='_prompt_container'] > div {
- margin: 0!important;
-}
diff --git a/test/basic_features/extras_test.py b/test/basic_features/extras_test.py index 0170c511..8ed98747 100644 --- a/test/basic_features/extras_test.py +++ b/test/basic_features/extras_test.py @@ -1,7 +1,9 @@ +import os import unittest import requests from gradio.processing_utils import encode_pil_to_base64 from PIL import Image +from modules.paths import script_path class TestExtrasWorking(unittest.TestCase): def setUp(self): @@ -19,7 +21,7 @@ class TestExtrasWorking(unittest.TestCase): "upscaler_1": "None", "upscaler_2": "None", "extras_upscaler_2_visibility": 0, - "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")) + "image": encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png"))) } def test_simple_upscaling_performed(self): @@ -31,7 +33,7 @@ class TestPngInfoWorking(unittest.TestCase): def setUp(self): self.url_png_info = "http://localhost:7860/sdapi/v1/extra-single-image" self.png_info = { - "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")) + "image": encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png"))) } def test_png_info_performed(self): @@ -42,7 +44,7 @@ class TestInterrogateWorking(unittest.TestCase): def setUp(self): self.url_interrogate = "http://localhost:7860/sdapi/v1/extra-single-image" self.interrogate = { - "image": encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png")), + "image": encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png"))), "model": "clip" } diff --git a/test/basic_features/img2img_test.py b/test/basic_features/img2img_test.py index 08c5c903..5240ec36 100644 --- a/test/basic_features/img2img_test.py +++ b/test/basic_features/img2img_test.py @@ -1,14 +1,16 @@ +import os import unittest import requests from gradio.processing_utils import encode_pil_to_base64 from PIL import Image +from modules.paths import script_path class TestImg2ImgWorking(unittest.TestCase): def setUp(self): self.url_img2img = "http://localhost:7860/sdapi/v1/img2img" self.simple_img2img = { - "init_images": [encode_pil_to_base64(Image.open(r"test/test_files/img2img_basic.png"))], + "init_images": [encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png")))], "resize_mode": 0, "denoising_strength": 0.75, "mask": None, @@ -47,11 +49,11 @@ class TestImg2ImgWorking(unittest.TestCase): self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200) def test_inpainting_masked_performed(self): - self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png")) + self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png"))) self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200) def test_inpainting_with_inverted_masked_performed(self): - self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png")) + self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(os.path.join(script_path, r"test/test_files/img2img_basic.png"))) self.simple_img2img["inpainting_mask_invert"] = True self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200) diff --git a/test/basic_features/txt2img_test.py b/test/basic_features/txt2img_test.py index 5aa43a44..cb525fbb 100644 --- a/test/basic_features/txt2img_test.py +++ b/test/basic_features/txt2img_test.py @@ -66,6 +66,8 @@ class TestTxt2ImgWorking(unittest.TestCase): self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) self.simple_txt2img["sampler_index"] = "DDIM" self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + self.simple_txt2img["sampler_index"] = "UniPC" + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) def test_txt2img_multiple_batches_performed(self): self.simple_txt2img["n_iter"] = 2 diff --git a/test/server_poll.py b/test/server_poll.py index 42d56a4c..c732630f 100644 --- a/test/server_poll.py +++ b/test/server_poll.py @@ -1,6 +1,8 @@ import unittest import requests import time +import os +from modules.paths import script_path def run_tests(proc, test_dir): @@ -15,8 +17,8 @@ def run_tests(proc, test_dir): break if proc.poll() is None: if test_dir is None: - test_dir = "test" - suite = unittest.TestLoader().discover(test_dir, pattern="*_test.py", top_level_dir="test") + test_dir = os.path.join(script_path, "test") + suite = unittest.TestLoader().discover(test_dir, pattern="*_test.py", top_level_dir=test_dir) result = unittest.TextTestRunner(verbosity=2).run(suite) return len(result.failures) + len(result.errors) else: @@ -12,11 +12,22 @@ from packaging import version import logging
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
-from modules import import_hook, errors, extra_networks, ui_extra_networks_checkpoints
-from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion
-from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
+from modules import paths, timer, import_hook, errors
+
+startup_timer = timer.Timer()
import torch
+startup_timer.record("import torch")
+
+import gradio
+startup_timer.record("import gradio")
+
+import ldm.modules.encoders.modules
+startup_timer.record("import ldm")
+
+from modules import extra_networks, ui_extra_networks_checkpoints
+from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion
+from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
if ".dev" in torch.__version__ or "+git" in torch.__version__:
@@ -30,7 +41,6 @@ import modules.gfpgan_model as gfpgan import modules.img2img
import modules.lowvram
-import modules.paths
import modules.scripts
import modules.sd_hijack
import modules.sd_models
@@ -45,6 +55,8 @@ from modules import modelloader from modules.shared import cmd_opts
import modules.hypernetworks.hypernetwork
+startup_timer.record("other imports")
+
if cmd_opts.server_name:
server_name = cmd_opts.server_name
@@ -88,6 +100,7 @@ def initialize(): extensions.list_extensions()
localization.list_localizations(cmd_opts.localizations_dir)
+ startup_timer.record("list extensions")
if cmd_opts.ui_debug_mode:
shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
@@ -96,16 +109,28 @@ def initialize(): modelloader.cleanup_models()
modules.sd_models.setup_model()
+ startup_timer.record("list SD models")
+
codeformer.setup_model(cmd_opts.codeformer_models_path)
+ startup_timer.record("setup codeformer")
+
gfpgan.setup_model(cmd_opts.gfpgan_models_path)
+ startup_timer.record("setup gfpgan")
modelloader.list_builtin_upscalers()
+ startup_timer.record("list builtin upscalers")
+
modules.scripts.load_scripts()
+ startup_timer.record("load scripts")
+
modelloader.load_upscalers()
+ startup_timer.record("load upscalers")
modules.sd_vae.refresh_vae_list()
+ startup_timer.record("refresh VAE")
modules.textual_inversion.textual_inversion.list_textual_inversion_templates()
+ startup_timer.record("refresh textual inversion templates")
try:
modules.sd_models.load_model()
@@ -114,6 +139,7 @@ def initialize(): print("", file=sys.stderr)
print("Stable diffusion model failed to load, exiting", file=sys.stderr)
exit(1)
+ startup_timer.record("load SD checkpoint")
shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
@@ -121,8 +147,10 @@ def initialize(): shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+ startup_timer.record("opts onchange")
shared.reload_hypernetworks()
+ startup_timer.record("reload hypernets")
ui_extra_networks.intialize()
ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())
@@ -131,6 +159,7 @@ def initialize(): extra_networks.initialize()
extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())
+ startup_timer.record("extra networks")
if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:
@@ -144,6 +173,7 @@ def initialize(): print("TLS setup invalid, running webui without TLS")
else:
print("Running with TLS")
+ startup_timer.record("TLS")
# make the program just exit at ctrl+c without waiting for anything
def sigint_handler(sig, frame):
@@ -153,13 +183,16 @@ def initialize(): signal.signal(signal.SIGINT, sigint_handler)
-def setup_cors(app):
+def setup_middleware(app):
+ app.middleware_stack = None # reset current middleware to allow modifying user provided list
+ app.add_middleware(GZipMiddleware, minimum_size=1000)
if cmd_opts.cors_allow_origins and cmd_opts.cors_allow_origins_regex:
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
elif cmd_opts.cors_allow_origins:
app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
elif cmd_opts.cors_allow_origins_regex:
app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
+ app.build_middleware_stack() # rebuild middleware stack on-the-fly
def create_api(app):
@@ -183,12 +216,12 @@ def api_only(): initialize()
app = FastAPI()
- setup_cors(app)
- app.add_middleware(GZipMiddleware, minimum_size=1000)
+ setup_middleware(app)
api = create_api(app)
modules.script_callbacks.app_started_callback(None, app)
+ print(f"Startup time: {startup_timer.summary()}.")
api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
@@ -199,14 +232,25 @@ def webui(): while 1:
if shared.opts.clean_temp_dir_at_start:
ui_tempdir.cleanup_tmpdr()
+ startup_timer.record("cleanup temp dir")
modules.script_callbacks.before_ui_callback()
+ startup_timer.record("scripts before_ui_callback")
shared.demo = modules.ui.create_ui()
+ startup_timer.record("create ui")
- if cmd_opts.gradio_queue:
+ if not cmd_opts.no_gradio_queue:
shared.demo.queue(64)
+ gradio_auth_creds = []
+ if cmd_opts.gradio_auth:
+ gradio_auth_creds += [x.strip() for x in cmd_opts.gradio_auth.strip('"').replace('\n', '').split(',') if x.strip()]
+ if cmd_opts.gradio_auth_path:
+ with open(cmd_opts.gradio_auth_path, 'r', encoding="utf8") as file:
+ for line in file.readlines():
+ gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
+
app, local_url, share_url = shared.demo.launch(
share=cmd_opts.share,
server_name=server_name,
@@ -214,22 +258,25 @@ def webui(): ssl_keyfile=cmd_opts.tls_keyfile,
ssl_certfile=cmd_opts.tls_certfile,
debug=cmd_opts.gradio_debug,
- auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None,
+ auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None,
inbrowser=cmd_opts.autolaunch,
prevent_thread_lock=True
)
+ for dep in shared.demo.dependencies:
+ dep['show_progress'] = False # disable gradio css animation on component update
+
# after initial launch, disable --autolaunch for subsequent restarts
cmd_opts.autolaunch = False
+ startup_timer.record("gradio launch")
+
# gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
# an attacker to trick the user into opening a malicious HTML page, which makes a request to the
# running web ui and do whatever the attacker wants, including installing an extension and
# running its code. We disable this here. Suggested by RyotaK.
app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
- setup_cors(app)
-
- app.add_middleware(GZipMiddleware, minimum_size=1000)
+ setup_middleware(app)
modules.progress.setup_progress_api(app)
@@ -239,28 +286,42 @@ def webui(): ui_extra_networks.add_pages_to_demo(app)
modules.script_callbacks.app_started_callback(shared.demo, app)
+ startup_timer.record("scripts app_started_callback")
+
+ print(f"Startup time: {startup_timer.summary()}.")
wait_on_server(shared.demo)
print('Restarting UI...')
+ startup_timer.reset()
+
sd_samplers.set_samplers()
modules.script_callbacks.script_unloaded_callback()
extensions.list_extensions()
+ startup_timer.record("list extensions")
localization.list_localizations(cmd_opts.localizations_dir)
modelloader.forbid_loaded_nonbuiltin_upscalers()
modules.scripts.reload_scripts()
+ startup_timer.record("load scripts")
+
modules.script_callbacks.model_loaded_callback(shared.sd_model)
+ startup_timer.record("model loaded callback")
+
modelloader.load_upscalers()
+ startup_timer.record("load upscalers")
for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
importlib.reload(module)
+ startup_timer.record("reload script modules")
modules.sd_models.list_models()
+ startup_timer.record("list SD models")
shared.reload_hypernetworks()
+ startup_timer.record("reload hypernetworks")
ui_extra_networks.intialize()
ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())
@@ -269,6 +330,7 @@ def webui(): extra_networks.initialize()
extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())
+ startup_timer.record("initialize extra networks")
if __name__ == "__main__":
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