Merge remote-tracking branch 'upstream/master'

.gitignore

@@ -29,3 +29,4 @@ notification.mp3
 /textual_inversion
 .vscode
 /extensions
+

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); 
-}
-
-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){
-        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, img_index, files){
-    return [
-        tabname, 
-        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),         
-        files
-    ];
-}
-
-function images_history_delete(del_num, tabname, 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;
-    del_num = Math.min(img_num - image_index, del_num)    
-    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++){
-            buttons[image_index + i].style.display = 'none';
-            buttons[image_index + i + img_num].style.display = 'none';
-            next_img = image_index + i + 1
-        }
-        var bnt;
-        if (next_img  >= img_num){
-            btn = buttons[image_index - 1];
-        } else {            
-            btn = buttons[next_img];          
-        } 
-        setTimeout(function(btn){btn.click()}, 30, btn);
-    }
-    images_history_disabled_del();  
-   
-}
-
-function images_history_turnpage(tabname){
-    gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled');
-    var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item");
-    buttons.forEach(function(elem) {
-        elem.style.display = 'block';
-    })   
-}
-
-function images_history_enable_del_buttons(){
-    gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){
-        btn.removeAttribute('disabled');
-    })
-}
-
-function images_history_init(){ 
-    var tabnames = gradioApp().getElementById("images_history_tabnames_list")   
-    if (tabnames){  
-        images_history_tab_list = tabnames.querySelector("textarea").value.split(",")    
-        for (var i in images_history_tab_list ){
-            var 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");  
-             gradioApp().getElementById(tab + "_images_history_start").setAttribute("style","padding:20px;font-size:25px");           
-        }
-
-        //preload
-        if (gradioApp().getElementById("images_history_preload").querySelector("input").checked ){
-            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);
-                tab_btns[i].addEventListener('click', function(){
-                    var tabs_box = gradioApp().getElementById("images_history_tab");
-                    if (!tabs_box.classList.contains(this.getAttribute("tabname"))) {
-                        gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click();
-                        tabs_box.classList.add(this.getAttribute("tabname"))
-                    }                
-                });
-            }            
-            tab_btns[0].click()
-        }
-    } else {
-        setTimeout(images_history_init, 500);
-    } 
-}
-
-var images_history_tab_list = "";
-setTimeout(images_history_init, 500);
-document.addEventListener("DOMContentLoaded", function() {
-    var mutationObserver = new MutationObserver(function(m){
-        if (images_history_tab_list != ""){
-            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);
-                });
-
-                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 });
-});
-
-

launch.py

@@ -111,7 +111,7 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
-    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26")
+    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@d91a2963bf87c6a770d74894667e9ffa9f6de7ff")
 
     xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
 

modules/api/models.py

 import inspect
-from pydantic import BaseModel, Field, Json, create_model
+from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore

modules/hypernetworks/hypernetwork.py

@@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
 
+from collections import defaultdict, deque
 from statistics import stdev, mean
 
 class HypernetworkModule(torch.nn.Module):
@@ -269,19 +270,18 @@ def stack_conds(conds):
     return torch.stack(conds)
 
 
-def log_statistics(loss_info:dict, key, value):
-    if key not in loss_info:
-        loss_info[key] = [value]
-    else:
-        loss_info[key].append(value)
-        if len(loss_info) > 1024:
-            loss_info.pop(0)
-
-
 def statistics(data):
-    total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})"
+    if len(data) < 2:
+        std = 0
+    else:
+        std = stdev(data)
+    total_information = f"loss:{mean(data):.3f}" + u"\u00B1" + f"({std/ (len(data) ** 0.5):.3f})"
     recent_data = data[-32:]
-    recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})"
+    if len(recent_data) < 2:
+        std = 0
+    else:
+        std = stdev(recent_data)
+    recent_information = f"recent 32 loss:{mean(recent_data):.3f}" + u"\u00B1" + f"({std / (len(recent_data) ** 0.5):.3f})"
     return total_information, recent_information
 
 
@@ -290,7 +290,7 @@ def report_statistics(loss_info:dict):
     for key in keys:
         try:
             print("Loss statistics for file " + key)
-            info, recent = statistics(loss_info[key])
+            info, recent = statistics(list(loss_info[key]))
             print(info)
             print(recent)
         except Exception as e:
@@ -341,8 +341,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         weight.requires_grad = True
 
     size = len(ds.indexes)
-    loss_dict = {}
+    loss_dict = defaultdict(lambda : deque(maxlen = 1024))
     losses = torch.zeros((size,))
+    previous_mean_losses = [0]
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
 
@@ -364,7 +365,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
         if len(loss_dict) > 0:
-            previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict)
+            previous_mean_losses = [i[-1] for i in loss_dict.values()]
+            previous_mean_loss = mean(previous_mean_losses)
             
         scheduler.apply(optimizer, hypernetwork.step)
         if scheduler.finished:
@@ -383,7 +385,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
             for entry in entries:
-                log_statistics(loss_dict, entry.filename, loss.item())
+                loss_dict[entry.filename].append(loss.item())
                 
             optimizer.zero_grad()
             weights[0].grad = None
@@ -399,7 +401,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
         if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
             raise RuntimeError("Loss diverged.")
-        pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}")
+        
+        if len(previous_mean_losses) > 1:
+            std = stdev(previous_mean_losses)
+        else:
+            std = 0
+        dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
+        pbar.set_description(dataset_loss_info)
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.

modules/processing.py

@@ -47,6 +47,25 @@ def apply_color_correction(correction, image):
     return image
 
 
+def apply_overlay(image, paste_loc, index, overlays):
+    if overlays is None or index >= len(overlays):
+        return image
+
+    overlay = overlays[index]
+
+    if paste_loc is not None:
+        x, y, w, h = paste_loc
+        base_image = Image.new('RGBA', (overlay.width, overlay.height))
+        image = images.resize_image(1, image, w, h)
+        base_image.paste(image, (x, y))
+        image = base_image
+
+    image = image.convert('RGBA')
+    image.alpha_composite(overlay)
+    image = image.convert('RGB')
+
+    return image
+
 def get_correct_sampler(p):
     if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):
         return sd_samplers.samplers
@@ -449,22 +468,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
-                        images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
+                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+                        images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                if p.overlay_images is not None and i < len(p.overlay_images):
-                    overlay = p.overlay_images[i]
-
-                    if p.paste_to is not None:
-                        x, y, w, h = p.paste_to
-                        base_image = Image.new('RGBA', (overlay.width, overlay.height))
-                        image = images.resize_image(1, image, w, h)
-                        base_image.paste(image, (x, y))
-                        image = base_image
-
-                    image = image.convert('RGBA')
-                    image.alpha_composite(overlay)
-                    image = image.convert('RGB')
+                image = apply_overlay(image, p.paste_to, i, p.overlay_images)
 
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)

modules/script_callbacks.py

@@ -16,7 +16,7 @@ def model_loaded_callback(sd_model):
 
 def ui_tabs_callback():
     res = []
-
+    
     for callback in callbacks_ui_tabs:
         res += callback() or []
 

modules/shared.py

@@ -80,12 +80,13 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = [
     "samples_filename_pattern",
+    "directories_filename_pattern",
     "outdir_samples",
     "outdir_txt2img_samples",
     "outdir_img2img_samples",
@@ -190,7 +191,8 @@ options_templates = {}
 options_templates.update(options_section(('saving-images', "Saving images/grids"), {
     "samples_save": OptionInfo(True, "Always save all generated images"),
     "samples_format": OptionInfo('png', 'File format for images'),
-    "samples_filename_pattern": OptionInfo("", "Images filename pattern"),
+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs),
+    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
 
     "grid_save": OptionInfo(True, "Always save all generated image grids"),
     "grid_format": OptionInfo('png', 'File format for grids'),
@@ -225,8 +227,8 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
     "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
     "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
     "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
-    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))
 
 options_templates.update(options_section(('upscaling', "Upscaling"), {
@@ -320,15 +322,6 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
     'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
 }))
 
-options_templates.update(options_section(('images-history', "Images Browser"), {
-    #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"),
-    "images_history_preload": OptionInfo(False, "Preload images at startup"),
-    "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"),
-    "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "),   
-    "images_history_grid_num": OptionInfo(6, "Number of grids in each row"),   
-
-}))
-
 
 class Options:
     data = None
@@ -357,7 +350,7 @@ class Options:
 
     def save(self, filename):
         with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file)
+            json.dump(self.data, file, indent=4)
 
     def same_type(self, x, y):
         if x is None or y is None:

modules/ui.py

@@ -38,7 +38,6 @@ import modules.codeformer_model
 import modules.generation_parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
-import modules.images_history as img_his
 import modules.ldsr_model
 import modules.scripts
 import modules.shared as shared
@@ -51,12 +50,11 @@ from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
 
-import modules.images_history as img_his
-
-
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
+txt2img_paste_fields = []
+img2img_paste_fields = []
 
 
 if not cmd_opts.share and not cmd_opts.listen:
@@ -786,6 +784,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
+            global txt2img_paste_fields 
             txt2img_paste_fields = [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
@@ -1056,6 +1055,7 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
+            global img2img_paste_fields
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
@@ -1104,9 +1104,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-
+                
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1193,15 +1193,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-    #images history
-    images_history_switch_dict = {
-        "fn": modules.generation_parameters_copypaste.connect_paste,
-        "t2i": txt2img_paste_fields,
-        "i2i": img2img_paste_fields
-    }
-
-    images_history = img_his.create_history_tabs(gr, opts, cmd_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):
             with gr.Column(variant='panel'):
@@ -1650,7 +1642,6 @@ Requested path was: {f}
         (img2img_interface, "img2img", "img2img"),
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
-        (images_history, "Image Browser", "images_history"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
         (train_interface, "Train", "ti"),
     ]
@@ -1894,6 +1885,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
+    
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"

webui.py

@@ -9,7 +9,7 @@ from fastapi.middleware.gzip import GZipMiddleware
 
 from modules.paths import script_path
 
-from modules import devices, sd_samplers
+from modules import devices, sd_samplers, upscaler
 import modules.codeformer_model as codeformer
 import modules.extras
 import modules.face_restoration
@@ -73,6 +73,11 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
 
 def initialize():
+    if cmd_opts.ui_debug_mode:
+        shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
+        modules.scripts.load_scripts()
+        return
+
     modelloader.cleanup_models()
     modules.sd_models.setup_model()
     codeformer.setup_model(cmd_opts.codeformer_models_path)