Merge branch 'master' into cuda-device-id-selection

.gitignore

@@ -27,4 +27,5 @@ __pycache__
 notification.mp3
 /SwinIR
 /textual_inversion
-.vscode
\ No newline at end of file
+.vscode
+/extensions

README.md

@@ -83,8 +83,17 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Estimated completion time in progress bar
 - API
 - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
-- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 
+## Where are Aesthetic Gradients?!?!
+Aesthetic Gradients are now an extension. You can install it using git:
+
+```commandline
+git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients
+```
+
+After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart
+the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.

javascript/images_history.js

@@ -17,14 +17,6 @@ var images_history_click_image = function(){
     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');
@@ -43,7 +35,6 @@ 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;
@@ -88,15 +79,15 @@ function images_history_set_image_info(button){
     
 }
 
-function images_history_get_current_img(tabname, image_path, files){
+function images_history_get_current_img(tabname, img_index, files){
     return [
-        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), 
-        image_path, 
+        tabname, 
+        gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"),         
         files
     ];
 }
 
-function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){
+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");
@@ -107,6 +98,7 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
         }
     });    
     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();
@@ -114,30 +106,28 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i
     } 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
-            }
+            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 - del_num];
+            btn = buttons[image_index - 1];
         } 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){
+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';
-    })
-    return [img_path, page_index, image_index, tabname];
+    })   
 }
 
 function images_history_enable_del_buttons(){
@@ -147,60 +137,64 @@ function images_history_enable_del_buttons(){
 }
 
 function images_history_init(){ 
-    var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page')
-    if (load_txt2img_button){        
+    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 ){
-            tab = images_history_tab_list[i];
+            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_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()
         }
-        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"];
+var images_history_tab_list = "";
 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);
-            }*/
-
-        }     
+        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 });
-
+    mutationObserver.observe(gradioApp(), { childList:true, subtree:true });
 });
 
 

modules/img2img.py

@@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, 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):
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: 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, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
     is_inpaint = mode == 1
     is_batch = mode == 2
 
@@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         inpainting_mask_invert=inpainting_mask_invert,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
+    p.scripts = modules.scripts.scripts_txt2img
+    p.script_args = args
 
     if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

modules/processing.py

@@ -104,6 +104,12 @@ class StableDiffusionProcessing():
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
+        self.scripts = None
+        self.script_args = None
+        self.all_prompts = None
+        self.all_seeds = None
+        self.all_subseeds = None
+
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
@@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     shared.prompt_styles.apply_styles(p)
 
     if type(p.prompt) == list:
-        all_prompts = p.prompt
+        p.all_prompts = p.prompt
     else:
-        all_prompts = p.batch_size * p.n_iter * [p.prompt]
+        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]
 
     if type(seed) == list:
-        all_seeds = seed
+        p.all_seeds = seed
     else:
-        all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))]
+        p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
 
     if type(subseed) == list:
-        all_subseeds = subseed
+        p.all_subseeds = subseed
     else:
-        all_subseeds = [int(subseed) + x for x in range(len(all_prompts))]
+        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0):
-        return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
+        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
+    if p.scripts is not None:
+        p.scripts.run_alwayson_scripts(p)
+
     infotexts = []
     output_images = []
 
     with torch.no_grad(), p.sd_model.ema_scope():
         with devices.autocast():
-            p.init(all_prompts, all_seeds, all_subseeds)
+            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
 
         if state.job_count == -1:
             state.job_count = p.n_iter
@@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if state.interrupted:
                 break
 
-            prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
-            seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
-            subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
+            prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
+            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
+            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
 
             if (len(prompts) == 0):
                 break
@@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 index_of_first_image = 1
 
             if opts.grid_save:
-                images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
+                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

modules/script_callbacks.py

+
+callbacks_model_loaded = []
+callbacks_ui_tabs = []
+
+
+def clear_callbacks():
+    callbacks_model_loaded.clear()
+    callbacks_ui_tabs.clear()
+
+
+def model_loaded_callback(sd_model):
+    for callback in callbacks_model_loaded:
+        callback(sd_model)
+
+
+def ui_tabs_callback():
+    res = []
+
+    for callback in callbacks_ui_tabs:
+        res += callback() or []
+
+    return res
+
+
+def on_model_loaded(callback):
+    """register a function to be called when the stable diffusion model is created; the model is
+    passed as an argument"""
+    callbacks_model_loaded.append(callback)
+
+
+def on_ui_tabs(callback):
+    """register a function to be called when the UI is creating new tabs.
+    The function must either return a None, which means no new tabs to be added, or a list, where
+    each element is a tuple:
+        (gradio_component, title, elem_id)
+
+    gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
+    title is tab text displayed to user in the UI
+    elem_id is HTML id for the tab
+    """
+    callbacks_ui_tabs.append(callback)
+

modules/sd_hijack.py

@@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                     multipliers.append([1.0] * 75)
 
             z1 = self.process_tokens(tokens, multipliers)
-            z1 = shared.aesthetic_clip(z1, remade_batch_tokens)
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
 
             remade_batch_tokens = rem_tokens

modules/sd_models.py

@@ -7,7 +7,7 @@ from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices
+from modules import shared, modelloader, devices, script_callbacks
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -238,6 +238,9 @@ def load_model(checkpoint_info=None):
     sd_hijack.model_hijack.hijack(sd_model)
 
     sd_model.eval()
+    shared.sd_model = sd_model
+
+    script_callbacks.model_loaded_callback(sd_model)
 
     print(f"Model loaded.")
     return sd_model
@@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None):
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         checkpoints_loaded.clear()
-        shared.sd_model = load_model(checkpoint_info)
+        load_model(checkpoint_info)
         return shared.sd_model
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

modules/shared.py

@@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)")
 parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
@@ -81,6 +80,7 @@ parser.add_argument("--disable-safe-unpickle", action='store_true', help="disabl
 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("--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 = [
@@ -109,21 +109,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
 loaded_hypernetwork = None
 
-
-os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True)
-aesthetic_embeddings = {}
-
-
-def update_aesthetic_embeddings():
-    global aesthetic_embeddings
-    aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
-                            os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
-    aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings)
-
-
-update_aesthetic_embeddings()
-
-
 def reload_hypernetworks():
     global hypernetworks
 
@@ -333,6 +318,14 @@ 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
@@ -407,9 +400,6 @@ sd_model = None
 
 clip_model = None
 
-from modules.aesthetic_clip import AestheticCLIP
-aesthetic_clip = AestheticCLIP()
-
 progress_print_out = sys.stdout
 
 

modules/txt2img.py

@@ -7,7 +7,7 @@ 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, 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):
+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, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         firstphase_height=firstphase_height if enable_hr else None,
     )
 
-    shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative)
+    p.scripts = modules.scripts.scripts_txt2img
+    p.script_args = args
 
     if cmd_opts.enable_console_prompts:
         print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

style.css

@@ -477,7 +477,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;

webui.py

@@ -71,6 +71,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
 
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
+
 def initialize():
     modelloader.cleanup_models()
     modules.sd_models.setup_model()
@@ -79,9 +80,9 @@ def initialize():
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
     modelloader.load_upscalers()
 
-    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+    modules.scripts.load_scripts()
 
-    shared.sd_model = modules.sd_models.load_model()
+    modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
@@ -145,7 +146,7 @@ def webui():
         sd_samplers.set_samplers()
 
         print('Reloading Custom Scripts')
-        modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
+        modules.scripts.reload_scripts()
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
         print('Refreshing Model List')