Merge branch 'master' into test_resolve_conflicts

.github/workflows/on_pull_request.yaml

+# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
+name: Run Linting/Formatting on Pull Requests
+
+on:
+  - push
+  - pull_request
+  # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
+  # if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
+  # pull_request:  
+  #  branches:
+  #    - master
+  #  branches-ignore:
+  #    - development
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v3
+        with:
+          python-version: 3.10.6
+      - name: Install PyLint
+        run: | 
+          python -m pip install --upgrade pip
+          pip install pylint
+      # This lets PyLint check to see if it can resolve imports
+      - name: Install dependencies
+        run : |
+          export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
+          python launch.py
+      - name: Analysing the code with pylint
+        run: |
+          pylint $(git ls-files '*.py')

.pylintrc

+# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
+[MESSAGES CONTROL]
+disable=C,R,W,E,I

javascript/dragdrop.js

@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap ) {
+    if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     e.stopPropagation();
@@ -53,6 +53,9 @@ window.document.addEventListener('dragover', e => {
 
 window.document.addEventListener('drop', e => {
     const target = e.composedPath()[0];
+    if (target.placeholder.indexOf("Prompt") == -1) {
+        return;
+    }
     const imgWrap = target.closest('[data-testid="image"]');
     if ( !imgWrap ) {
         return;

javascript/edit-attention.js

@@ -2,6 +2,8 @@ addEventListener('keydown', (event) => {
 	let target = event.originalTarget || event.composedPath()[0];
 	if (!target.hasAttribute("placeholder")) return;
 	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+	if (! (event.metaKey || event.ctrlKey)) return;
+
 
 	let plus = "ArrowUp"
 	let minus = "ArrowDown"

javascript/hints.js

@@ -16,6 +16,8 @@ titles = {
     "\u{1f3a8}": "Add a random artist to the prompt.",
     "\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{1f4cb}": "Apply selected styles to current prompt",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
@@ -87,8 +89,8 @@ titles = {
 
     "Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.",
 
-    "Weighted Sum": "Result = A * (1 - M) + B * M",
-    "Add difference": "Result = A + (B - C) * (1 - M)",
+    "Weighted sum": "Result = A * (1 - M) + B * M",
+    "Add difference": "Result = A + (B - C) * M",
 }
 
 

javascript/imageParams.js

+window.onload = (function(){
+    window.addEventListener('drop', e => {
+        const target = e.composedPath()[0];
+        const idx = selected_gallery_index();
+        if (target.placeholder.indexOf("Prompt") == -1) return;
+
+        let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
+
+        e.stopPropagation();
+        e.preventDefault();
+        const imgParent = gradioApp().getElementById(prompt_target);
+        const files = e.dataTransfer.files;
+        const fileInput = imgParent.querySelector('input[type="file"]');
+        if ( fileInput ) {
+            fileInput.files = files;
+            fileInput.dispatchEvent(new Event('change'));
+        }
+    });
+});

javascript/images_history.js

+var images_history_click_image = function(){
+    if (!this.classList.contains("transform")){        
+        var gallery = images_history_get_parent_by_class(this, "images_history_cantainor");
+        var buttons = gallery.querySelectorAll(".gallery-item");
+        var i = 0;
+        var hidden_list = [];
+        buttons.forEach(function(e){
+            if (e.style.display == "none"){
+                hidden_list.push(i);
+            }
+            i += 1;
+        })
+        if (hidden_list.length > 0){
+            setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
+        }        
+    }    
+    images_history_set_image_info(this); 
+}
+
+var images_history_click_tab = function(){
+    var tabs_box = gradioApp().getElementById("images_history_tab");
+    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
+        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click();
+        tabs_box.classList.add(this.getAttribute("tabname"))
+    }                
+}
+
+function images_history_disabled_del(){
+    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
+        btn.setAttribute('disabled','disabled');
+    }); 
+}
+
+function images_history_get_parent_by_class(item, class_name){
+    var parent = item.parentElement;
+    while(!parent.classList.contains(class_name)){
+        parent = parent.parentElement;
+    }
+    return parent;  
+}
+
+function images_history_get_parent_by_tagname(item, tagname){
+    var parent = item.parentElement;
+    tagname = tagname.toUpperCase()
+    while(parent.tagName != tagname){
+        console.log(parent.tagName, tagname)
+        parent = parent.parentElement;
+    }  
+    return parent;
+}
+
+function images_history_hide_buttons(hidden_list, gallery){
+    var buttons = gallery.querySelectorAll(".gallery-item");
+    var num = 0;
+    buttons.forEach(function(e){
+        if (e.style.display == "none"){
+            num += 1;
+        }
+    });
+    if (num == hidden_list.length){
+        setTimeout(images_history_hide_buttons, 10, hidden_list, gallery);
+    } 
+    for( i in hidden_list){
+        buttons[hidden_list[i]].style.display = "none";
+    }    
+}
+
+function images_history_set_image_info(button){
+    var buttons = images_history_get_parent_by_tagname(button, "DIV").querySelectorAll(".gallery-item");
+    var index = -1;
+    var i = 0;
+    buttons.forEach(function(e){
+        if(e == button){
+            index = i;
+        }
+        if(e.style.display != "none"){
+            i += 1;
+        }        
+    });
+    var gallery = images_history_get_parent_by_class(button, "images_history_cantainor");
+    var set_btn = gallery.querySelector(".images_history_set_index");
+    var curr_idx = set_btn.getAttribute("img_index", index);  
+    if (curr_idx != index) {
+        set_btn.setAttribute("img_index", index);        
+        images_history_disabled_del();
+    }
+    set_btn.click();
+    
+}
+
+function images_history_get_current_img(tabname, image_path, files){
+    return [
+        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), 
+        image_path, 
+        files
+    ];
+}
+
+function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
+    image_index = parseInt(image_index);
+    var tab = gradioApp().getElementById(tabname + '_images_history');
+    var set_btn = tab.querySelector(".images_history_set_index");
+    var buttons = [];
+    tab.querySelectorAll(".gallery-item").forEach(function(e){
+        if (e.style.display != 'none'){
+            buttons.push(e);
+        }
+    });    
+    var img_num = buttons.length / 2;
+    if (img_num <= del_num){
+        setTimeout(function(tabname){
+            gradioApp().getElementById(tabname + '_images_history_renew_page').click();
+        }, 30, tabname); 
+    } else {
+        var next_img  
+        for (var i = 0; i < del_num; i++){
+            if (image_index + i < image_index + img_num){
+                buttons[image_index + i].style.display = 'none';
+                buttons[image_index + img_num + 1].style.display = 'none';
+                next_img = image_index + i + 1
+            }
+        }
+        var bnt;
+        if (next_img  >= img_num){
+            btn = buttons[image_index - del_num];
+        } else {            
+            btn = buttons[next_img];          
+        } 
+        setTimeout(function(btn){btn.click()}, 30, btn);
+    }
+    images_history_disabled_del();  
+    return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index];
+}
+
+function images_history_turnpage(img_path, page_index, image_index, tabname){
+    var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
+    buttons.forEach(function(elem) {
+        elem.style.display = 'block';
+    })
+    return [img_path, page_index, image_index, tabname];
+}
+
+function images_history_enable_del_buttons(){
+    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
+        btn.removeAttribute('disabled');
+    })
+}
+
+function images_history_init(){ 
+    var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
+    if (load_txt2img_button){        
+        for (var i in images_history_tab_list ){
+            tab = images_history_tab_list[i];
+            gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor");
+            gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index");
+            gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button");
+            gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery");            
+                     
+        }
+        var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div");
+        tabs_box.setAttribute("id", "images_history_tab");        
+        var tab_btns = tabs_box.querySelectorAll("button");        
+        for (var i in images_history_tab_list){               
+            var tabname = images_history_tab_list[i]
+            tab_btns[i].setAttribute("tabname", tabname);
+
+            // this refreshes history upon tab switch
+            // until the history is known to work well, which is not the case now, we do not do this at startup
+            //tab_btns[i].addEventListener('click', images_history_click_tab);
+        }    
+        tabs_box.classList.add(images_history_tab_list[0]);
+
+        // same as above, at page load
+        //load_txt2img_button.click();
+    } else {
+        setTimeout(images_history_init, 500);
+    } 
+}
+
+var images_history_tab_list = ["txt2img", "img2img", "extras"];
+setTimeout(images_history_init, 500);
+document.addEventListener("DOMContentLoaded", function() {
+    var mutationObserver = new MutationObserver(function(m){
+        for (var i in images_history_tab_list ){
+            let tabname = images_history_tab_list[i]
+            var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item');
+            buttons.forEach(function(bnt){    
+                bnt.addEventListener('click', images_history_click_image, true);
+            });
+
+            // same as load_txt2img_button.click() above
+            /*
+            var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg");
+            if (cls_btn){
+                cls_btn.addEventListener('click', function(){
+                    gradioApp().getElementById(tabname + '_images_history_renew_page').click();
+                }, false);
+            }*/
+
+        }     
+    });
+    mutationObserver.observe( gradioApp(), { childList:true, subtree:true });
+
+});
+
+

javascript/progressbar.js

 // code related to showing and updating progressbar shown as the image is being made
 global_progressbars = {}
+galleries = {}
+galleryObservers = {}
 
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
     var progressbar = gradioApp().getElementById(id_progressbar)
@@ -31,13 +33,24 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                 preview.style.width = gallery.clientWidth + "px"
                 preview.style.height = gallery.clientHeight + "px"
 
+				//only watch gallery if there is a generation process going on
+                check_gallery(id_gallery);    
+
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
                 if(!progressDiv){
                     if (skip) {
                         skip.style.display = "none"
                     }
                     interrupt.style.display = "none"
+			
+                    //disconnect observer once generation finished, so user can close selected image if they want
+                    if (galleryObservers[id_gallery]) {
+                        galleryObservers[id_gallery].disconnect();
+                        galleries[id_gallery] = null;
+                    }    
                 }
+
+
             }
 
             window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
@@ -46,6 +59,28 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 	}
 }
 
+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();
+        }
+        let prevSelectedIndex = selected_gallery_index();
+        galleryObservers[id_gallery] = new MutationObserver(function (){
+            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
+            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
+            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
+                //automatically re-open previously selected index (if exists)
+                galleryButtons[prevSelectedIndex].click();
+		showGalleryImage();
+            }
+        })
+        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
+    }
+}
+
 onUiUpdate(function(){
     check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
     check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')

javascript/ui.js

@@ -141,7 +141,7 @@ function submit_img2img(){
 
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     name_ = prompt('Style name:')
-    return name_ === null ? [null, null, null]: [name_, prompt_text, negative_prompt_text]
+    return [name_, prompt_text, negative_prompt_text]
 }
 
 
@@ -187,12 +187,10 @@ onUiUpdate(function(){
 	if (!txt2img_textarea) {
 		txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
 		txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
-        txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
 	}
 	if (!img2img_textarea) {
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
-        img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate"));
 	}
 })
 
@@ -220,14 +218,6 @@ function update_token_counter(button_id) {
 	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
 
-function submit_prompt(event, generate_button_id) {
-    if (event.altKey && event.keyCode === 13) {
-        event.preventDefault();
-        gradioApp().getElementById(generate_button_id).click();
-        return;
-    }
-}
-
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
     setTimeout(function(){location.reload()},2000)

launch.py

@@ -9,6 +9,7 @@ import platform
 dir_repos = "repositories"
 python = sys.executable
 git = os.environ.get('GIT', "git")
+index_url = os.environ.get('INDEX_URL', "")
 
 
 def extract_arg(args, name):
@@ -57,7 +58,8 @@ def run_python(code, desc=None, errdesc=None):
 
 
 def run_pip(args, desc=None):
-    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
 
 
 def check_run_python(code):
@@ -76,7 +78,7 @@ def git_clone(url, dir, name, commithash=None):
             return
 
         run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
-        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
+        run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
         return
 
     run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")

modules/deepbooru.py

@@ -102,7 +102,7 @@ def get_deepbooru_tags_model():
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
-        model_path, compile_model=True
+        model_path, compile_model=False
     )
     return model, tags
 

modules/devices.py

@@ -34,7 +34,7 @@ def enable_tf32():
 
 errors.run(enable_tf32, "Enabling TF32")
 
-device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
+device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
 dtype = torch.float16
 dtype_vae = torch.float16
 

modules/extras.py

@@ -159,24 +159,12 @@ def run_pnginfo(image):
     return '', geninfo, info
 
 
-def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, interp_amount, save_as_half, custom_name):
-    # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
     def weighted_sum(theta0, theta1, theta2, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
-    # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def sigmoid(theta0, theta1, theta2, alpha):
-        alpha = alpha * alpha * (3 - (2 * alpha))
-        return theta0 + ((theta1 - theta0) * alpha)
-
-    # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
-    def inv_sigmoid(theta0, theta1, theta2, alpha):
-        import math
-        alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
-        return theta0 + ((theta1 - theta0) * alpha)
-
     def add_difference(theta0, theta1, theta2, alpha):
-        return theta0 + (theta1 - theta2) * (1.0 - alpha)
+        return theta0 + (theta1 - theta2) * alpha
 
     primary_model_info = sd_models.checkpoints_list[primary_model_name]
     secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
@@ -198,9 +186,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
         theta_2 = None
 
     theta_funcs = {
-        "Weighted Sum": weighted_sum,
-        "Sigmoid": sigmoid,
-        "Inverse Sigmoid": inv_sigmoid,
+        "Weighted sum": weighted_sum,
         "Add difference": add_difference,
     }
     theta_func = theta_funcs[interp_method]
@@ -209,7 +195,12 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     for key in tqdm.tqdm(theta_0.keys()):
         if 'model' in key and key in theta_1:
-            theta_0[key] = theta_func(theta_0[key], theta_1[key], theta_2[key] if theta_2 else None, (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+            t2 = (theta_2 or {}).get(key)
+            if t2 is None:
+                t2 = torch.zeros_like(theta_0[key])
+
+            theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
+
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
 
@@ -222,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_nam
 
     ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
 
-    filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+    filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
     filename = filename if custom_name == '' else (custom_name + '.ckpt')
     output_modelname = os.path.join(ckpt_dir, filename)
 

modules/hypernetworks/hypernetwork.py

@@ -5,6 +5,7 @@ import os
 import sys
 import traceback
 import tqdm
+import csv
 
 import torch
 
@@ -14,6 +15,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
+from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
 
@@ -180,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
+def stack_conds(conds):
+    if len(conds) == 1:
+        return torch.stack(conds)
+
+    # same as in reconstruct_multicond_batch
+    token_count = max([x.shape[0] for x in conds])
+    for i in range(len(conds)):
+        if conds[i].shape[0] != token_count:
+            last_vector = conds[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
+            conds[i] = torch.vstack([conds[i], last_vector_repeated])
+
+    return torch.stack(conds)
+
+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):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -209,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     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=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+        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)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -233,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
         scheduler.apply(optimizer, hypernetwork.step)
@@ -244,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            cond = entry.cond.to(devices.device)
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
+#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
             del x
-            del cond
+            del c
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
@@ -262,23 +279,39 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
+        textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
+
         if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             optimizer.zero_grad()
             shared.sd_model.cond_stage_model.to(devices.device)
             shared.sd_model.first_stage_model.to(devices.device)
 
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0] if len(processed.images)>0 else None
 
@@ -297,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {hypernetwork.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>

modules/images.py

 import datetime
+import io
 import math
 import os
 from collections import namedtuple
@@ -23,6 +24,10 @@ def image_grid(imgs, batch_size=1, rows=None):
             rows = opts.n_rows
         elif opts.n_rows == 0:
             rows = batch_size
+        elif opts.grid_prevent_empty_spots:
+            rows = math.floor(math.sqrt(len(imgs)))
+            while len(imgs) % rows != 0:
+                rows -= 1
         else:
             rows = math.sqrt(len(imgs))
             rows = round(rows)
@@ -463,3 +468,22 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn = None
 
     return fullfn, txt_fullfn
+
+
+def image_data(data):
+    try:
+        image = Image.open(io.BytesIO(data))
+        textinfo = image.text["parameters"]
+        return textinfo, None
+    except Exception:
+        pass
+
+    try:
+        text = data.decode('utf8')
+        assert len(text) < 10000
+        return text, None
+
+    except Exception:
+        pass
+
+    return '', None

modules/images_history.py

+import os
+import shutil
+
+
+def traverse_all_files(output_dir, image_list, curr_dir=None):
+    curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir)
+    try:
+        f_list = os.listdir(curr_path)
+    except:
+        if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt":
+            image_list.append(curr_dir)
+        return image_list
+    for file in f_list:
+        file = file if curr_dir is None else os.path.join(curr_dir, file)
+        file_path = os.path.join(curr_path, file)
+        if file[-4:] == ".txt":
+            pass
+        elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0:
+            image_list.append(file)
+        else:
+            image_list = traverse_all_files(output_dir, image_list, file)
+    return image_list
+
+
+def get_recent_images(dir_name, page_index, step, image_index, tabname):
+    page_index = int(page_index)
+    f_list = os.listdir(dir_name)
+    image_list = []
+    image_list = traverse_all_files(dir_name, image_list)
+    image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file)))
+    num = 48 if tabname != "extras" else 12
+    max_page_index = len(image_list) // num + 1
+    page_index = max_page_index if page_index == -1 else page_index + step
+    page_index = 1 if page_index < 1 else page_index
+    page_index = max_page_index if page_index > max_page_index else page_index
+    idx_frm = (page_index - 1) * num
+    image_list = image_list[idx_frm:idx_frm + num]
+    image_index = int(image_index)
+    if image_index < 0 or image_index > len(image_list) - 1:
+        current_file = None
+        hidden = None
+    else:
+        current_file = image_list[int(image_index)]
+        hidden = os.path.join(dir_name, current_file)
+    return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, ""
+
+
+def first_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, 1, 0, image_index, tabname)
+
+
+def end_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, -1, 0, image_index, tabname)
+
+
+def prev_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, -1, image_index, tabname)
+
+
+def next_page_click(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, 1, image_index, tabname)
+
+
+def page_index_change(dir_name, page_index, image_index, tabname):
+    return get_recent_images(dir_name, page_index, 0, image_index, tabname)
+
+
+def show_image_info(num, image_path, filenames):
+    # print(f"select image {num}")
+    file = filenames[int(num)]
+    return file, num, os.path.join(image_path, file)
+
+
+def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index):
+    if name == "":
+        return filenames, delete_num
+    else:
+        delete_num = int(delete_num)
+        index = list(filenames).index(name)
+        i = 0
+        new_file_list = []
+        for name in filenames:
+            if i >= index and i < index + delete_num:
+                path = os.path.join(dir_name, name)
+                if os.path.exists(path):
+                    print(f"Delete file {path}")
+                    os.remove(path)
+                    txt_file = os.path.splitext(path)[0] + ".txt"
+                    if os.path.exists(txt_file):
+                        os.remove(txt_file)
+                else:
+                    print(f"Not exists file {path}")
+            else:
+                new_file_list.append(name)
+            i += 1
+    return new_file_list, 1
+
+
+def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
+    if opts.outdir_samples != "":
+        dir_name = opts.outdir_samples
+    elif tabname == "txt2img":
+        dir_name = opts.outdir_txt2img_samples
+    elif tabname == "img2img":
+        dir_name = opts.outdir_img2img_samples
+    elif tabname == "extras":
+        dir_name = opts.outdir_extras_samples
+    d = dir_name.split("/")
+    dir_name = "/" if dir_name.startswith("/") else d[0]
+    for p in d[1:]:
+        dir_name = os.path.join(dir_name, p)
+    with gr.Row():
+        renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page")
+        first_page = gr.Button('First Page')
+        prev_page = gr.Button('Prev Page')
+        page_index = gr.Number(value=1, label="Page Index")
+        next_page = gr.Button('Next Page')
+        end_page = gr.Button('End Page')
+    with gr.Row(elem_id=tabname + "_images_history"):
+        with gr.Row():
+            with gr.Column(scale=2):
+                history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6)
+                with gr.Row():
+                    delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next")
+                    delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button")
+            with gr.Column():
+                with gr.Row():
+                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
+                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                with gr.Row():
+                    with gr.Column():
+                        img_file_info = gr.Textbox(label="Generate Info", interactive=False)
+                        img_file_name = gr.Textbox(label="File Name", interactive=False)
+                with gr.Row():
+                    # hiden items
+
+                    img_path = gr.Textbox(dir_name.rstrip("/"), visible=False)
+                    tabname_box = gr.Textbox(tabname, visible=False)
+                    image_index = gr.Textbox(value=-1, visible=False)
+                    set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False)
+                    filenames = gr.State()
+                    hidden = gr.Image(type="pil", visible=False)
+                    info1 = gr.Textbox(visible=False)
+                    info2 = gr.Textbox(visible=False)
+
+    # turn pages
+    gallery_inputs = [img_path, page_index, image_index, tabname_box]
+    gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name]
+
+    first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs)
+    # page_index.change(page_index_change, inputs=[tabname_box, img_path,  page_index], outputs=[history_gallery, page_index])
+
+    # other funcitons
+    set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden])
+    img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None)
+    delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num])
+    hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+
+    # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2])
+    switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img')
+    switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img')
+
+
+def create_history_tabs(gr, opts, run_pnginfo, switch_dict):
+    with gr.Blocks(analytics_enabled=False) as images_history:
+        with gr.Tabs() as tabs:
+            with gr.Tab("txt2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_txt2img:
+                    show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict)
+            with gr.Tab("img2img history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict)
+            with gr.Tab("extras history"):
+                with gr.Blocks(analytics_enabled=False) as images_history_img2img:
+                    show_images_history(gr, opts, "extras", run_pnginfo, switch_dict)
+    return images_history

modules/interrogate.py

@@ -55,7 +55,7 @@ class InterrogateModels:
 
         model, preprocess = clip.load(clip_model_name)
         model.eval()
-        model = model.to(shared.device)
+        model = model.to(devices.device_interrogate)
 
         return model, preprocess
 
@@ -65,14 +65,14 @@ class InterrogateModels:
             if not shared.cmd_opts.no_half:
                 self.blip_model = self.blip_model.half()
 
-        self.blip_model = self.blip_model.to(shared.device)
+        self.blip_model = self.blip_model.to(devices.device_interrogate)
 
         if self.clip_model is None:
             self.clip_model, self.clip_preprocess = self.load_clip_model()
             if not shared.cmd_opts.no_half:
                 self.clip_model = self.clip_model.half()
 
-        self.clip_model = self.clip_model.to(shared.device)
+        self.clip_model = self.clip_model.to(devices.device_interrogate)
 
         self.dtype = next(self.clip_model.parameters()).dtype
 
@@ -99,11 +99,11 @@ class InterrogateModels:
             text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
 
         top_count = min(top_count, len(text_array))
-        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(shared.device)
+        text_tokens = clip.tokenize([text for text in text_array], truncate=True).to(devices.device_interrogate)
         text_features = self.clip_model.encode_text(text_tokens).type(self.dtype)
         text_features /= text_features.norm(dim=-1, keepdim=True)
 
-        similarity = torch.zeros((1, len(text_array))).to(shared.device)
+        similarity = torch.zeros((1, len(text_array))).to(devices.device_interrogate)
         for i in range(image_features.shape[0]):
             similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
         similarity /= image_features.shape[0]
@@ -116,7 +116,7 @@ class InterrogateModels:
             transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
             transforms.ToTensor(),
             transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
-        ])(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+        ])(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
         with torch.no_grad():
             caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
@@ -140,7 +140,7 @@ class InterrogateModels:
 
             res = caption
 
-            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(shared.device)
+            clip_image = self.clip_preprocess(pil_image).unsqueeze(0).type(self.dtype).to(devices.device_interrogate)
 
             precision_scope = torch.autocast if shared.cmd_opts.precision == "autocast" else contextlib.nullcontext
             with torch.no_grad(), precision_scope("cuda"):

modules/processing.py

@@ -145,9 +145,8 @@ class Processed:
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
-        self.subseed = int(
-            self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
+        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
+        self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
@@ -541,16 +540,15 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
-    firstphase_width = 0
-    firstphase_height = 0
-    firstphase_width_truncated = 0
-    firstphase_height_truncated = 0
 
-    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):
+    def __init__(self, enable_hr=False, denoising_strength=0.75, firstphase_width=0, firstphase_height=0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
-        self.scale_latent = scale_latent
         self.denoising_strength = denoising_strength
+        self.firstphase_width = firstphase_width
+        self.firstphase_height = firstphase_height
+        self.truncate_x = 0
+        self.truncate_y = 0
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
@@ -559,14 +557,31 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             else:
                 state.job_count = state.job_count * 2
 
+            if self.firstphase_width == 0 or self.firstphase_height == 0:
                 desired_pixel_count = 512 * 512
                 actual_pixel_count = self.width * self.height
                 scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-
                 self.firstphase_width = math.ceil(scale * self.width / 64) * 64
                 self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-            self.firstphase_width_truncated = int(scale * self.width)
-            self.firstphase_height_truncated = int(scale * self.height)
+                firstphase_width_truncated = int(scale * self.width)
+                firstphase_height_truncated = int(scale * self.height)
+
+            else:
+
+                width_ratio = self.width / self.firstphase_width
+                height_ratio = self.height / self.firstphase_height
+
+                if width_ratio > height_ratio:
+                    firstphase_width_truncated = self.firstphase_width
+                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
+                else:
+                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
+                    firstphase_height_truncated = self.firstphase_height
+
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
+            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
+            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
@@ -585,22 +600,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                                   seed_resize_from_w=self.seed_resize_from_w, p=self)
         samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
 
-        truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
-        truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
-
-        samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2,
-                  truncate_x // 2:samples.shape[3] - truncate_x // 2]
+        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-        if self.scale_latent:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
-                                                      mode="bilinear")
+        if opts.use_scale_latent_for_hires_fix:
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
-
-            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-                decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width),
-                                                                  mode="bilinear")
-            else:
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
             batch_images = []

modules/safe.py

@@ -96,11 +96,18 @@ def load(filename, *args, **kwargs):
         if not shared.cmd_opts.disable_safe_unpickle:
             check_pt(filename)
 
+    except pickle.UnpicklingError:
+        print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        print(f"-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
+        return None
+
     except Exception:
         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
         print(f"\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
-        print(f"You can skip this check with --disable-safe-unpickle commandline argument.", file=sys.stderr)
+        print(f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
         return None
 
     return unsafe_torch_load(filename, *args, **kwargs)

modules/sd_models.py

-import glob
+import collections
 import os.path
 import sys
 from collections import namedtuple
@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
 checkpoints_list = {}
+checkpoints_loaded = collections.OrderedDict()
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -132,14 +133,14 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
+    if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    pl_sd = torch.load(checkpoint_file, map_location="cpu")
+        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
         if "global_step" in pl_sd:
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-
         model.load_state_dict(sd, strict=False)
 
         if shared.cmd_opts.opt_channelslast:
@@ -158,13 +159,20 @@ def load_model_weights(model, checkpoint_info):
 
         if os.path.exists(vae_file):
             print(f"Loading VAE weights from: {vae_file}")
-        vae_ckpt = torch.load(vae_file, map_location="cpu")
+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
             vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
-
             model.first_stage_model.load_state_dict(vae_dict)
 
         model.first_stage_model.to(devices.dtype_vae)
 
+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
+    else:
+        print(f"Loading weights [{sd_model_hash}] from cache")
+        checkpoints_loaded.move_to_end(checkpoint_info)
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
@@ -202,6 +210,7 @@ def reload_model_weights(sd_model, info=None):
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+        checkpoints_loaded.clear()
         shared.sd_model = load_model()
         return shared.sd_model
 

modules/shared.py

@@ -36,6 +36,7 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
@@ -56,7 +57,7 @@ parser.add_argument("--opt-split-attention", action='store_true', help="force-en
 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("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
+parser.add_argument("--use-cpu", nargs='+',choices=['all', 'sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'], help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
 parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
@@ -78,10 +79,11 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 
 cmd_opts = parser.parse_args()
 
-devices.device, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
-(devices.cpu if x in cmd_opts.use_cpu else devices.get_optimal_device() for x in ['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'])
+devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_bsrgan, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
+(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'bsrgan', 'esrgan', 'scunet', 'codeformer'])
 
 device = devices.device
+weight_load_location = None if cmd_opts.lowram else "cpu"
 
 batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
 parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
@@ -184,6 +186,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "grid_format": OptionInfo('png', 'File format for grids'),
     "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
     "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
     "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
 
     "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
@@ -224,6 +227,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -242,11 +246,13 @@ options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
 }))
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
@@ -260,7 +266,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -288,6 +293,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {

modules/textual_inversion/dataset.py

@@ -24,11 +24,12 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
+    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):
+        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
 
+        self.batch_size = batch_size
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -78,13 +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)
 
-        self.length = len(self.dataset) * repeats
+        assert len(self.dataset) > 1, "No images have been found in the dataset."
+        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()
 
@@ -101,13 +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

modules/textual_inversion/textual_inversion.py

@@ -6,6 +6,7 @@ import torch
 import tqdm
 import html
 import datetime
+import csv
 
 from PIL import Image, PngImagePlugin
 
@@ -172,15 +173,33 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def batched(dataset, total, n=1):
-    for ndx in range(0, total, n):
-        yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
+def write_loss(log_directory, filename, step, epoch_len, values):
+    if shared.opts.training_write_csv_every == 0:
+        return
+
+    if step % shared.opts.training_write_csv_every != 0:
+        return
+
+    write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
+
+    with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
+        csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
+
+        if write_csv_header:
+            csv_writer.writeheader()
+
+        epoch = step // epoch_len
+        epoch_step = step - epoch * epoch_len
+
+        csv_writer.writerow({
+            "step": step + 1,
+            "epoch": epoch + 1,
+            "epoch_step": epoch_step + 1,
+            **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_image_prompt, batch_size=1,
-                    gradient_accumulation=1):
+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..."
@@ -212,11 +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
 
@@ -235,8 +250,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
-    pbar = tqdm.tqdm(enumerate(batched(ds, steps - ititial_step, batch_size)), total=steps - ititial_step)
-    for i, entry in pbar:
+    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
+    for i, entries in pbar:
         embedding.step = i + ititial_step
 
         scheduler.apply(optimizer, embedding.step)
@@ -247,11 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([e.cond_text for e in entry])
-
-            x = torch.stack([e.latent for e in entry]).to(devices.device)
+            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()
@@ -271,21 +284,37 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
 
+        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+            "loss": f"{losses.mean():.7f}",
+            "learn_rate": scheduler.learn_rate
+        })
+
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry[0].cond_text if preview_image_prompt == "" else preview_image_prompt
-
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                height=training_height,
-                width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
 
+            if preview_from_txt2img:
+                p.prompt = preview_prompt
+                p.negative_prompt = preview_negative_prompt
+                p.steps = preview_steps
+                p.sampler_index = preview_sampler_index
+                p.cfg_scale = preview_cfg_scale
+                p.seed = preview_seed
+                p.width = preview_width
+                p.height = preview_height
+            else:
+                p.prompt = entries[0].cond_text
+                p.steps = 20
+                p.width = training_width
+                p.height = training_height
+
+            preview_text = p.prompt
+
             processed = processing.process_images(p)
             image = processed.images[0]
 
@@ -320,7 +349,7 @@ 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[-1].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>

modules/txt2img.py

@@ -6,18 +6,13 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int,
-            restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int,
-            subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool,
-            height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float,
-            aesthetic_lr=0,
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int,aesthetic_lr=0,
             aesthetic_weight=0, aesthetic_steps=0,
             aesthetic_imgs=None,
             aesthetic_slerp=False,
             aesthetic_imgs_text="",
             aesthetic_slerp_angle=0.15,
-            aesthetic_text_negative=False,
-            *args):
+            aesthetic_text_negative=False, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -41,8 +36,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         restore_faces=restore_faces,
         tiling=tiling,
         enable_hr=enable_hr,
-        scale_latent=scale_latent if enable_hr else None,
         denoising_strength=denoising_strength if enable_hr else None,
+        firstphase_width=firstphase_width if enable_hr else None,
+        firstphase_height=firstphase_height if enable_hr else None,
     )
 
     if cmd_opts.enable_console_prompts:

requirements.txt

@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.4.1
+gradio==3.5
 invisible-watermark
 numpy
 omegaconf

requirements_versions.txt

@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.4.1
+gradio==3.5
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0

script.js

@@ -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]');

scripts/prompts_from_file.py

+import copy
 import math
 import os
 import sys
 import traceback
+import shlex
 
 import modules.scripts as scripts
 import gradio as gr
@@ -10,6 +12,75 @@ from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
+
+def process_string_tag(tag):
+    return tag
+
+
+def process_int_tag(tag):
+    return int(tag)
+
+
+def process_float_tag(tag):
+    return float(tag)
+
+
+def process_boolean_tag(tag):
+    return True if (tag == "true") else False
+
+
+prompt_tags = {
+    "sd_model": None,
+    "outpath_samples": process_string_tag,
+    "outpath_grids": process_string_tag,
+    "prompt_for_display": process_string_tag,
+    "prompt": process_string_tag,
+    "negative_prompt": process_string_tag,
+    "styles": process_string_tag,
+    "seed": process_int_tag,
+    "subseed_strength": process_float_tag,
+    "subseed": process_int_tag,
+    "seed_resize_from_h": process_int_tag,
+    "seed_resize_from_w": process_int_tag,
+    "sampler_index": process_int_tag,
+    "batch_size": process_int_tag,
+    "n_iter": process_int_tag,
+    "steps": process_int_tag,
+    "cfg_scale": process_float_tag,
+    "width": process_int_tag,
+    "height": process_int_tag,
+    "restore_faces": process_boolean_tag,
+    "tiling": process_boolean_tag,
+    "do_not_save_samples": process_boolean_tag,
+    "do_not_save_grid": process_boolean_tag
+}
+
+
+def cmdargs(line):
+    args = shlex.split(line)
+    pos = 0
+    res = {}
+
+    while pos < len(args):
+        arg = args[pos]
+
+        assert arg.startswith("--"), f'must start with "--": {arg}'
+        tag = arg[2:]
+
+        func = prompt_tags.get(tag, None)
+        assert func, f'unknown commandline option: {arg}'
+
+        assert pos+1 < len(args), f'missing argument for command line option {arg}'
+
+        val = args[pos+1]
+
+        res[tag] = func(val)
+
+        pos += 2
+
+    return res
+
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
@@ -32,26 +103,48 @@ class Script(scripts.Script):
         return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
 
     def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
-        if (checkbox_txt):
+        if checkbox_txt:
             lines = [x.strip() for x in prompt_txt.splitlines()]
         else:
             lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
         lines = [x for x in lines if len(x) > 0]
 
-        img_count = len(lines) * p.n_iter
-        batch_count = math.ceil(img_count / p.batch_size)
-        loop_count = math.ceil(batch_count / p.n_iter)
-        print(f"Will process {img_count} images in {batch_count} batches.")
-
         p.do_not_save_grid = True
 
-        state.job_count = batch_count
+        job_count = 0
+        jobs = []
+
+        for line in lines:
+            if "--" in line:
+                try:
+                    args = cmdargs(line)
+                except Exception:
+                    print(f"Error parsing line [line] as commandline:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+                    args = {"prompt": line}
+            else:
+                args = {"prompt": line}
+
+            n_iter = args.get("n_iter", 1)
+            if n_iter != 1:
+                job_count += n_iter
+            else:
+                job_count += 1
+
+            jobs.append(args)
+
+        print(f"Will process {len(lines)} lines in {job_count} jobs.")
+        state.job_count = job_count
 
         images = []
-        for loop_no in range(loop_count):
-            state.job = f"{loop_no + 1} out of {loop_count}"
-            p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
-            proc = process_images(p)
+        for n, args in enumerate(jobs):
+            state.job = f"{state.job_no + 1} out of {state.job_count}"
+
+            copy_p = copy.copy(p)
+            for k, v in args.items():
+                setattr(copy_p, k, v)
+
+            proc = process_images(copy_p)
             images += proc.images
 
         return Processed(p, images, p.seed, "")

scripts/xy_grid.py

@@ -12,7 +12,7 @@ import gradio as gr
 
 from modules import images
 from modules.hypernetworks import hypernetwork
-from modules.processing import process_images, Processed, get_correct_sampler
+from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -176,7 +176,7 @@ axis_options = [
     AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
+    AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
 ]
 
 
@@ -338,7 +338,7 @@ class Script(scripts.Script):
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-            if axis_opt.label == 'Seed':
+            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]
             else:
                 return axis_list
@@ -354,6 +354,9 @@ class Script(scripts.Script):
         else:
             total_steps = p.steps * len(xs) * len(ys)
 
+        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
+            total_steps *= 2
+
         print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
         shared.total_tqdm.updateTotal(total_steps * p.n_iter)
 

style.css

@@ -115,7 +115,7 @@
     padding: 0.4em 0;
 }
 
-#roll, #paste{
+#roll, #paste, #style_create, #style_apply{
     min-width: 2em;
     min-height: 2em;
     max-width: 2em;
@@ -126,14 +126,14 @@
     margin: 0.1em 0;
 }
 
-#style_apply, #style_create, #interrogate{
-    margin: 0.75em 0.25em 0.25em 0.25em;
-    min-width: 5em;
+#interrogate_col{
+    min-width: 0 !important;
+    max-width: 8em !important;
 }
-
-#style_apply, #style_create, #deepbooru{
-    margin: 0.75em 0.25em 0.25em 0.25em;
-    min-width: 5em;
+#interrogate, #deepbooru{
+    margin: 0em 0.25em 0.9em 0.25em;
+    min-width: 8em;
+    max-width: 8em;
 }
 
 #style_pos_col, #style_neg_col{
@@ -167,18 +167,6 @@ button{
     align-self: stretch !important;
 }
 
-#prompt, #negative_prompt{
-    border: none !important;
-}
-#prompt textarea, #negative_prompt textarea{
-    border: none !important;
-}
-
-
-#img2maskimg .h-60{
-    height: 30rem;
-}
-
 .overflow-hidden, .gr-panel{
     overflow: visible !important;
 }
@@ -451,10 +439,6 @@ input[type="range"]{
     --tw-bg-opacity: 0 !important;
 }
 
-#img2img_image div.h-60{
-    height: 480px;
-}
-
 #context-menu{
     z-index:9999;
     position:absolute;
@@ -529,3 +513,11 @@ canvas[key="mask"] {
 .row.gr-compact{
     overflow: visible;
 }
+
+#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
+img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img
+{
+    height: 480px !important;
+    max-height: 480px !important;
+    min-height: 480px !important;
+}
\ No newline at end of file

webui.sh

@@ -82,8 +82,8 @@ then
     clone_dir="${PWD##*/}"
 fi
 
-# Check prequisites
-for preq in git python3
+# Check prerequisites
+for preq in "${GIT}" "${python_cmd}"
 do
     if ! hash "${preq}" &>/dev/null
     then