mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-01-01 20:35:06 +08:00
Merge branch 'master' into master
This commit is contained in:
AUTOMATIC1111
GitHub
commit
ea8aa1701a
@ -163,10 +163,15 @@ function images_history_init(){
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for (var i in images_history_tab_list){
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var tabname = images_history_tab_list[i]
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tab_btns[i].setAttribute("tabname", tabname);
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tab_btns[i].addEventListener('click', images_history_click_tab);
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// this refreshes history upon tab switch
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// until the history is known to work well, which is not the case now, we do not do this at startup
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//tab_btns[i].addEventListener('click', images_history_click_tab);
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}
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tabs_box.classList.add(images_history_tab_list[0]);
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load_txt2img_button.click();
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// same as above, at page load
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//load_txt2img_button.click();
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} else {
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setTimeout(images_history_init, 500);
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}
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@ -182,12 +187,15 @@ document.addEventListener("DOMContentLoaded", function() {
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buttons.forEach(function(bnt){
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bnt.addEventListener('click', images_history_click_image, true);
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});
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// same as load_txt2img_button.click() above
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/*
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var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
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if (cls_btn){
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cls_btn.addEventListener('click', function(){
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gradioApp().getElementById(tabname + '_images_history_renew_page').click();
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}, false);
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}
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}*/
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}
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});
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@ -1,5 +1,7 @@
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// code related to showing and updating progressbar shown as the image is being made
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global_progressbars = {}
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galleries = {}
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galleryObservers = {}
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function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
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var progressbar = gradioApp().getElementById(id_progressbar)
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@ -31,21 +33,54 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
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preview.style.width = gallery.clientWidth + "px"
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preview.style.height = gallery.clientHeight + "px"
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//only watch gallery if there is a generation process going on
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check_gallery(id_gallery);
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var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
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if(!progressDiv){
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if (skip) {
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skip.style.display = "none"
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}
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interrupt.style.display = "none"
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//disconnect observer once generation finished, so user can close selected image if they want
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if (galleryObservers[id_gallery]) {
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galleryObservers[id_gallery].disconnect();
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galleries[id_gallery] = null;
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}
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}
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}
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window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
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});
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mutationObserver.observe( progressbar, { childList:true, subtree:true })
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}
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}
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function check_gallery(id_gallery){
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let gallery = gradioApp().getElementById(id_gallery)
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// if gallery has no change, no need to setting up observer again.
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if (gallery && galleries[id_gallery] !== gallery){
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galleries[id_gallery] = gallery;
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if(galleryObservers[id_gallery]){
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galleryObservers[id_gallery].disconnect();
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}
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let prevSelectedIndex = selected_gallery_index();
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galleryObservers[id_gallery] = new MutationObserver(function (){
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let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
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let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
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if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
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//automatically re-open previously selected index (if exists)
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galleryButtons[prevSelectedIndex].click();
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showGalleryImage();
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}
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})
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galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
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}
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}
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onUiUpdate(function(){
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check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
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check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')
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@ -187,12 +187,10 @@ onUiUpdate(function(){
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if (!txt2img_textarea) {
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txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
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txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
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txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
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}
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if (!img2img_textarea) {
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img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
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img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
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img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate"));
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}
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})
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@ -220,14 +218,6 @@ function update_token_counter(button_id) {
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token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
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}
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function submit_prompt(event, generate_button_id) {
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if (event.altKey && event.keyCode === 13) {
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event.preventDefault();
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gradioApp().getElementById(generate_button_id).click();
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return;
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}
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}
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function restart_reload(){
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document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
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setTimeout(function(){location.reload()},2000)
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@ -9,6 +9,7 @@ import platform
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dir_repos = "repositories"
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python = sys.executable
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git = os.environ.get('GIT', "git")
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index_url = os.environ.get('INDEX_URL',"")
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def extract_arg(args, name):
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@ -57,7 +58,7 @@ def run_python(code, desc=None, errdesc=None):
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def run_pip(args, desc=None):
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return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
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return run(f'"{python}" -m pip {args} --prefer-binary{f' --index-url {index_url}' if index_url!='' else ''}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
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def check_run_python(code):
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@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
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return self.to_out(out)
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def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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def stack_conds(conds):
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if len(conds) == 1:
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return torch.stack(conds)
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# same as in reconstruct_multicond_batch
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token_count = max([x.shape[0] for x in conds])
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for i in range(len(conds)):
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if conds[i].shape[0] != token_count:
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last_vector = conds[i][-1:]
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last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
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conds[i] = torch.vstack([conds[i], last_vector_repeated])
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return torch.stack(conds)
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def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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assert hypernetwork_name, 'hypernetwork not selected'
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path = shared.hypernetworks.get(hypernetwork_name, None)
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@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
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ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
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if unload:
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shared.sd_model.cond_stage_model.to(devices.cpu)
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@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
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pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
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for i, entry in pbar:
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for i, entries in pbar:
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hypernetwork.step = i + ititial_step
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scheduler.apply(optimizer, hypernetwork.step)
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@ -246,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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break
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with torch.autocast("cuda"):
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cond = entry.cond.to(devices.device)
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x = entry.latent.to(devices.device)
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loss = shared.sd_model(x.unsqueeze(0), cond)[0]
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c = stack_conds([entry.cond for entry in entries]).to(devices.device)
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# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
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x = torch.stack([entry.latent for entry in entries]).to(devices.device)
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loss = shared.sd_model(x, c)[0]
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del x
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del cond
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del c
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losses[hypernetwork.step % losses.shape[0]] = loss.item()
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@ -292,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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p.width = preview_width
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p.height = preview_height
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else:
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p.prompt = entry.cond_text
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p.prompt = entries[0].cond_text
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p.steps = 20
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preview_text = p.prompt
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@ -315,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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<p>
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Loss: {losses.mean():.7f}<br/>
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Step: {hypernetwork.step}<br/>
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Last prompt: {html.escape(entry.cond_text)}<br/>
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Last prompt: {html.escape(entries[0].cond_text)}<br/>
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Last saved embedding: {html.escape(last_saved_file)}<br/>
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Last saved image: {html.escape(last_saved_image)}<br/>
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</p>
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@ -1,5 +1,7 @@
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import os
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import shutil
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def traverse_all_files(output_dir, image_list, curr_dir=None):
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curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
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try:
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@ -42,21 +44,33 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname):
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hidden = os.path.join(dir_name, current_file)
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return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
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def first_page_click(dir_name, page_index, image_index, tabname):
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return get_recent_images(dir_name, 1, 0, image_index, tabname)
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def end_page_click(dir_name, page_index, image_index, tabname):
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return get_recent_images(dir_name, -1, 0, image_index, tabname)
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def prev_page_click(dir_name, page_index, image_index, tabname):
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return get_recent_images(dir_name, page_index, -1, image_index, tabname)
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def next_page_click(dir_name, page_index, image_index, tabname):
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return get_recent_images(dir_name, page_index, 1, image_index, tabname)
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def page_index_change(dir_name, page_index, image_index, tabname):
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return get_recent_images(dir_name, page_index, 0, image_index, tabname)
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def show_image_info(num, image_path, filenames):
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# print(f"select image {num}")
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file = filenames[int(num)]
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return file, num, os.path.join(image_path, file)
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def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
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if name == "":
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return filenames, delete_num
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@ -81,15 +95,18 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
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i += 1
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return new_file_list, 1
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def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
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if tabname == "txt2img":
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if opts.outdir_samples != "":
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dir_name = opts.outdir_samples
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elif tabname == "txt2img":
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dir_name = opts.outdir_txt2img_samples
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elif tabname == "img2img":
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dir_name = opts.outdir_img2img_samples
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elif tabname == "extras":
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dir_name = opts.outdir_extras_samples
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d = dir_name.split("/")
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dir_name = d[0]
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dir_name = "/" if dir_name.startswith("/") else d[0]
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for p in d[1:]:
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dir_name = os.path.join(dir_name, p)
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with gr.Row():
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@ -126,7 +143,6 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
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info1 = gr.Textbox(visible=False)
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info2 = gr.Textbox(visible=False)
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# turn pages
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gallery_inputs = [img_path, page_index, image_index, tabname_box]
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gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
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@ -140,7 +140,7 @@ class Processed:
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self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
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self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
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self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
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self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
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self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
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self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
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self.all_prompts = all_prompts or [self.prompt]
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@ -24,11 +24,12 @@ class DatasetEntry:
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||||
|
||||
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class PersonalizedBase(Dataset):
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def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
|
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def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
|
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re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
|
||||
|
||||
self.placeholder_token = placeholder_token
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||||
|
||||
self.batch_size = batch_size
|
||||
self.width = width
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||||
self.height = height
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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@ -78,14 +79,14 @@ class PersonalizedBase(Dataset):
|
||||
|
||||
if include_cond:
|
||||
entry.cond_text = self.create_text(filename_text)
|
||||
entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
|
||||
entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
|
||||
|
||||
self.dataset.append(entry)
|
||||
|
||||
assert len(self.dataset) > 1, "No images have been found in the dataset."
|
||||
self.length = len(self.dataset) * repeats
|
||||
self.length = len(self.dataset) * repeats // batch_size
|
||||
|
||||
self.initial_indexes = np.arange(self.length) % len(self.dataset)
|
||||
self.initial_indexes = np.arange(len(self.dataset))
|
||||
self.indexes = None
|
||||
self.shuffle()
|
||||
|
||||
@ -102,13 +103,19 @@ class PersonalizedBase(Dataset):
|
||||
return self.length
|
||||
|
||||
def __getitem__(self, i):
|
||||
if i % len(self.dataset) == 0:
|
||||
res = []
|
||||
|
||||
for j in range(self.batch_size):
|
||||
position = i * self.batch_size + j
|
||||
if position % len(self.indexes) == 0:
|
||||
self.shuffle()
|
||||
|
||||
index = self.indexes[i % len(self.indexes)]
|
||||
index = self.indexes[position % len(self.indexes)]
|
||||
entry = self.dataset[index]
|
||||
|
||||
if entry.cond is None:
|
||||
entry.cond_text = self.create_text(entry.filename_text)
|
||||
|
||||
return entry
|
||||
res.append(entry)
|
||||
|
||||
return res
|
||||
|
||||
@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
|
||||
})
|
||||
|
||||
|
||||
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
|
||||
assert embedding_name, 'embedding not selected'
|
||||
|
||||
shared.state.textinfo = "Initializing textual inversion training..."
|
||||
@ -231,7 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
||||
|
||||
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||
with torch.autocast("cuda"):
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
|
||||
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
|
||||
|
||||
hijack = sd_hijack.model_hijack
|
||||
|
||||
@ -251,7 +251,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
||||
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
|
||||
|
||||
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
|
||||
for i, entry in pbar:
|
||||
for i, entries in pbar:
|
||||
embedding.step = i + ititial_step
|
||||
|
||||
scheduler.apply(optimizer, embedding.step)
|
||||
@ -262,10 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
||||
break
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
c = cond_model([entry.cond_text])
|
||||
|
||||
x = entry.latent.to(devices.device)
|
||||
loss = shared.sd_model(x.unsqueeze(0), c)[0]
|
||||
c = cond_model([entry.cond_text for entry in entries])
|
||||
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
|
||||
loss = shared.sd_model(x, c)[0]
|
||||
del x
|
||||
|
||||
losses[embedding.step % losses.shape[0]] = loss.item()
|
||||
@ -307,7 +306,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
||||
p.width = preview_width
|
||||
p.height = preview_height
|
||||
else:
|
||||
p.prompt = entry.cond_text
|
||||
p.prompt = entries[0].cond_text
|
||||
p.steps = 20
|
||||
p.width = training_width
|
||||
p.height = training_height
|
||||
@ -348,7 +347,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
|
||||
<p>
|
||||
Loss: {losses.mean():.7f}<br/>
|
||||
Step: {embedding.step}<br/>
|
||||
Last prompt: {html.escape(entry.cond_text)}<br/>
|
||||
Last prompt: {html.escape(entries[0].cond_text)}<br/>
|
||||
Last saved embedding: {html.escape(last_saved_file)}<br/>
|
||||
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||
</p>
|
||||
|
||||
@ -433,7 +433,10 @@ def create_toprow(is_img2img):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=80):
|
||||
with gr.Row():
|
||||
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
|
||||
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
|
||||
placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
|
||||
)
|
||||
|
||||
with gr.Column(scale=1, elem_id="roll_col"):
|
||||
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
|
||||
paste = gr.Button(value=paste_symbol, elem_id="paste")
|
||||
@ -446,7 +449,10 @@ def create_toprow(is_img2img):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=8):
|
||||
with gr.Row():
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
|
||||
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)"
|
||||
)
|
||||
|
||||
with gr.Column(scale=1, elem_id="roll_col"):
|
||||
sh = gr.Button(elem_id="sh", visible=True)
|
||||
|
||||
@ -1090,7 +1096,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
"i2i":img2img_paste_fields
|
||||
}
|
||||
|
||||
#images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
|
||||
images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
|
||||
|
||||
with gr.Blocks() as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
@ -1166,6 +1172,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
||||
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
|
||||
learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
|
||||
batch_size = gr.Number(label='Batch size', value=1, precision=0)
|
||||
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
||||
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
|
||||
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
|
||||
@ -1244,6 +1251,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
inputs=[
|
||||
train_embedding_name,
|
||||
learn_rate,
|
||||
batch_size,
|
||||
dataset_directory,
|
||||
log_directory,
|
||||
training_width,
|
||||
@ -1268,6 +1276,7 @@ def create_ui(wrap_gradio_gpu_call):
|
||||
inputs=[
|
||||
train_hypernetwork_name,
|
||||
learn_rate,
|
||||
batch_size,
|
||||
dataset_directory,
|
||||
log_directory,
|
||||
steps,
|
||||
@ -1487,7 +1496,7 @@ Requested path was: {f}
|
||||
(img2img_interface, "img2img", "img2img"),
|
||||
(extras_interface, "Extras", "extras"),
|
||||
(pnginfo_interface, "PNG Info", "pnginfo"),
|
||||
#(images_history, "History", "images_history"),
|
||||
(images_history, "History", "images_history"),
|
||||
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
|
||||
(train_interface, "Train", "ti"),
|
||||
(settings_interface, "Settings", "settings"),
|
||||
|
||||
@ -50,9 +50,9 @@ document.addEventListener("DOMContentLoaded", function() {
|
||||
document.addEventListener('keydown', function(e) {
|
||||
var handled = false;
|
||||
if (e.key !== undefined) {
|
||||
if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
|
||||
if((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
|
||||
} else if (e.keyCode !== undefined) {
|
||||
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
|
||||
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
|
||||
}
|
||||
if (handled) {
|
||||
button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
|
||||
|
||||
Loading…
Reference in New Issue
Block a user