Merge branch 'dev' into torch

extensions-builtin/Lora/extra_networks_lora.py

@@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
-        if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
             p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 

extensions-builtin/Lora/scripts/lora_script.py

@@ -52,5 +52,5 @@ script_callbacks.on_before_ui(before_ui)
 
 
 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
 }))

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -5,11 +5,15 @@ import traceback
 import PIL.Image
 import numpy as np
 import torch
+from tqdm import tqdm
+
 from basicsr.utils.download_util import load_file_from_url
 
 import modules.upscaler
 from modules import devices, modelloader
 from scunet_model_arch import SCUNet as net
+from modules.shared import opts
+from modules import images
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             scalers.append(scaler_data2)
         self.scalers = scalers
 
-    def do_upscale(self, img: PIL.Image, selected_file):
+    @staticmethod
+    @torch.no_grad()
+    def tiled_inference(img, model):
+        # test the image tile by tile
+        h, w = img.shape[2:]
+        tile = opts.SCUNET_tile
+        tile_overlap = opts.SCUNET_tile_overlap
+        if tile == 0:
+            return model(img)
+
+        device = devices.get_device_for('scunet')
+        assert tile % 8 == 0, "tile size should be a multiple of window_size"
+        sf = 1
+
+        stride = tile - tile_overlap
+        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+            for h_idx in h_idx_list:
+
+                for w_idx in w_idx_list:
+
+                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+                    out_patch = model(in_patch)
+                    out_patch_mask = torch.ones_like(out_patch)
+
+                    E[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch)
+                    W[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch_mask)
+                    pbar.update(1)
+        output = E.div_(W)
+
+        return output
+
+    def do_upscale(self, img: PIL.Image.Image, selected_file):
+
         torch.cuda.empty_cache()
 
         model = self.load_model(selected_file)
         if model is None:
+            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
             return img
 
         device = devices.get_device_for('scunet')
-        img = np.array(img)
+        tile = opts.SCUNET_tile
-        img = img[:, :, ::-1]
+        h, w = img.height, img.width
-        img = np.moveaxis(img, 2, 0) / 255
+        np_img = np.array(img)
-        img = torch.from_numpy(img).float()
+        np_img = np_img[:, :, ::-1]  # RGB to BGR
-        img = img.unsqueeze(0).to(device)
+        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
 
-        with torch.no_grad():
+        if tile > h or tile > w:
-            output = model(img)
+            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+            _img[:, :, :h, :w] = torch_img # pad image
-        output = 255. * np.moveaxis(output, 0, 2)
+            torch_img = _img
-        output = output.astype(np.uint8)
+
-        output = output[:, :, ::-1]
+        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+        del torch_img, torch_output
         torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
+
+        output = np_output.transpose((1, 2, 0))  # CHW to HWC
+        output = output[:, :, ::-1]  # BGR to RGB
+        return PIL.Image.fromarray((output * 255).astype(np.uint8))
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = model.to(device)
 
         return model
-

javascript/contextMenus.js

@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2];
   appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
   appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
-  appendContextMenuOption('#roll','Roll three',
-    function(){
-      let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
-      setTimeout(function(){rollbutton.click()},100)
-      setTimeout(function(){rollbutton.click()},200)
-      setTimeout(function(){rollbutton.click()},300)
-    }
-  )
 })();
 //End example Context Menu Items
 

javascript/edit-attention.js

@@ -17,7 +17,7 @@ function keyupEditAttention(event){
 		// Find opening parenthesis around current cursor
 		const before = text.substring(0, selectionStart);
 		let beforeParen = before.lastIndexOf(OPEN);
-		if (beforeParen == -1) return  false;
+		if (beforeParen == -1) return false;
 		let beforeParenClose = before.lastIndexOf(CLOSE);
 		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
 			beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
@@ -27,7 +27,7 @@ function keyupEditAttention(event){
 		// Find closing parenthesis around current cursor
 		const after = text.substring(selectionStart);
 		let afterParen = after.indexOf(CLOSE);
-		if (afterParen == -1) return  false;
+		if (afterParen == -1) return false;
 		let afterParenOpen = after.indexOf(OPEN);
 		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
 			afterParen = after.indexOf(CLOSE, afterParen + 1);
@@ -43,10 +43,28 @@ function keyupEditAttention(event){
 		target.setSelectionRange(selectionStart, selectionEnd);
 		return true;
     }
+    
+    function selectCurrentWord(){
+        if (selectionStart !== selectionEnd) return false;
+        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        
+        // seek backward until to find beggining
+        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+            selectionStart--;
+        }
+        
+        // seek forward to find end
+        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+            selectionEnd++;
+        }
 
-	// If the user hasn't selected anything, let's select their current parenthesis block
+        target.setSelectionRange(selectionStart, selectionEnd);
-    if(! selectCurrentParenthesisBlock('<', '>')){
+        return true;
-        selectCurrentParenthesisBlock('(', ')')
+    }
+
+	// If the user hasn't selected anything, let's select their current parenthesis block or word
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+        selectCurrentWord();
     }
 
 	event.preventDefault();
@@ -81,7 +99,13 @@ function keyupEditAttention(event){
 	weight = parseFloat(weight.toPrecision(12));
 	if(String(weight).length == 1) weight += ".0"
 
-	text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    if (closeCharacter == ')' && weight == 1) {
+        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+        selectionStart--;
+        selectionEnd--;
+    } else {
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    }
 
 	target.focus();
 	target.value = text;
@@ -93,4 +117,4 @@ function keyupEditAttention(event){
 
 addEventListener('keydown', (event) => {
     keyupEditAttention(event);
-});
+});

javascript/generationParams.js

@@ -16,7 +16,7 @@ onUiUpdate(function(){
 
 let modalObserver = new MutationObserver(function(mutations) {
 	mutations.forEach(function(mutationRecord) {
-		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
+		let selectedTab = gradioApp().querySelector('#tabs div button')?.innerText
 		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
 			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
 	});

javascript/imageviewer.js

@@ -251,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() {
 
     modal.appendChild(modalNext)
 
-    gradioApp().appendChild(modal)
+    try {
-
+		gradioApp().appendChild(modal);
+	} catch (e) {
+		gradioApp().body.appendChild(modal);
+	}
 
     document.body.appendChild(modal);
 

javascript/progressbar.js

@@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 return
             }
 
-            if(elapsedFromStart > 5 && !res.queued && !res.active){
+            if(elapsedFromStart > 40 && !res.queued && !res.active){
                 removeProgressBar()
                 return
             }

modules/api/api.py

@@ -6,7 +6,6 @@ import uvicorn
 import gradio as gr
 from threading import Lock
 from io import BytesIO
-from gradio.processing_utils import decode_base64_to_file
 from fastapi import APIRouter, Depends, FastAPI, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from fastapi.exceptions import HTTPException
@@ -395,16 +394,11 @@ class Api:
     def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
         reqDict = setUpscalers(req)
 
-        def prepareFiles(file):
+        image_list = reqDict.pop('imageList', [])
-            file = decode_base64_to_file(file.data, file_path=file.name)
+        image_folder = [decode_base64_to_image(x.data) for x in image_list]
-            file.orig_name = file.name
-            return file
-
-        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
-        reqDict.pop('imageList')
 
         with self.queue_lock:
-            result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
+            result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)
 
         return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 

modules/devices.py

@@ -92,14 +92,18 @@ def cond_cast_float(input):
 
 
 def randn(seed, shape):
+    from modules.shared import opts
+
     torch.manual_seed(seed)
-    if device.type == 'mps':
+    if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 
 
 def randn_without_seed(shape):
-    if device.type == 'mps':
+    from modules.shared import opts
+
+    if opts.randn_source == "CPU" or device.type == 'mps':
         return torch.randn(shape, device=cpu).to(device)
     return torch.randn(shape, device=device)
 

modules/extra_networks_hypernet.py

@@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
     def activate(self, p, params_list):
         additional = shared.opts.sd_hypernetwork
 
-        if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
             p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
 

modules/generation_parameters_copypaste.py

@@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
     restore_old_hires_fix_params(res)
 
+    # Missing RNG means the default was set, which is GPU RNG
+    if "RNG" not in res:
+        res["RNG"] = "GPU"
+
     return res
 
 
@@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [
     ('UniPC skip type', 'uni_pc_skip_type'),
     ('UniPC order', 'uni_pc_order'),
     ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('RNG', 'randn_source'),
 ]
 
 

modules/images.py

@@ -352,6 +352,7 @@ class FilenameGenerator:
         'prompt_no_styles': lambda self: self.prompt_no_style(),
         'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
         'prompt_words': lambda self: self.prompt_words(),
+        'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
     }
     default_time_format = '%Y%m%d%H%M%S'
 

modules/img2img.py

@@ -151,7 +151,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         override_settings=override_settings,
     )
 
-    p.scripts = modules.scripts.scripts_txt2img
+    p.scripts = modules.scripts.scripts_img2img
     p.script_args = args
 
     if shared.cmd_opts.enable_console_prompts:

modules/interrogate.py

@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
     category_types = ["artists", "flavors", "mediums", "movements"]
 
     try:
-        os.makedirs(tmpdir)
+        os.makedirs(tmpdir, exist_ok=True)
         for category_type in category_types:
             torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
         os.rename(tmpdir, content_dir)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
         errors.display(e, "downloading default CLIP interrogate categories")
     finally:
         if os.path.exists(tmpdir):
-            os.remove(tmpdir)
+            os.removedirs(tmpdir)
 
 
 class InterrogateModels:

modules/postprocessing.py

@@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     if extras_mode == 1:
         for img in image_folder:
-            image = Image.open(img)
+            if isinstance(img, Image.Image):
+                image = img
+                fn = ''
+            else:
+                image = Image.open(os.path.abspath(img.name))
+                fn = os.path.splitext(img.orig_name)[0]
+
             image_data.append(image)
-            image_names.append(os.path.splitext(img.orig_name)[0])
+            image_names.append(fn)
     elif extras_mode == 2:
         assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
         assert input_dir, 'input directory not selected'

modules/processing.py

@@ -3,6 +3,7 @@ import math
 import os
 import sys
 import warnings
+import hashlib
 
 import torch
 import numpy as np
@@ -476,6 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
         "Clip skip": None if clip_skip <= 1 else clip_skip,
         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "Init image hash": getattr(p, 'init_img_hash', None),
+        "RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
     }
 
     generation_params.update(p.extra_generation_params)
@@ -1007,6 +1010,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.color_corrections = []
         imgs = []
         for img in self.init_images:
+
+            # Save init image
+            if opts.save_init_img:
+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
             image = images.flatten(img, opts.img2img_background_color)
 
             if crop_region is None and self.resize_mode != 3:

modules/sd_samplers_common.py

@@ -60,3 +60,13 @@ def store_latent(decoded):
 
 class InterruptedException(BaseException):
     pass
+
+
+if opts.randn_source == "CPU":
+    import torchsde._brownian.brownian_interval
+
+    def torchsde_randn(size, dtype, device, seed):
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+    torchsde._brownian.brownian_interval._randn = torchsde_randn

modules/sd_samplers_kdiffusion.py

@@ -190,7 +190,7 @@ class TorchHijack:
             if noise.shape == x.shape:
                 return noise
 
-        if x.device.type == 'mps':
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
             return torch.randn_like(x, device=devices.cpu).to(x.device)
         else:
             return torch.randn_like(x)

modules/shared.py

@@ -39,6 +39,7 @@ restricted_opts = {
     "outdir_grids",
     "outdir_txt2img_grids",
     "outdir_save",
+    "outdir_init_images"
 }
 
 ui_reorder_categories = [
@@ -253,6 +254,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
     "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
     "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+    "save_init_img": OptionInfo(False, "Save init images when using img2img"),
 
     "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
     "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
@@ -268,6 +270,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
     "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
     "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
     "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+    "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
 }))
 
 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
@@ -283,6 +286,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -331,6 +336,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -361,7 +367,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
     "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
     "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {
@@ -382,6 +388,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
     "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+    "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
     "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
     "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
     "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),

modules/ui.py

@@ -171,8 +171,8 @@ def create_seed_inputs(target_interface):
     with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
         seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
         seed.style(container=False)
-        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
+        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
 
         seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
 
@@ -468,7 +468,7 @@ def create_ui():
                                 height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
 
                             with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
-                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
 
                             if opts.dimensions_and_batch_together:
                                 with gr.Column(elem_id="txt2img_column_batch"):
@@ -1204,7 +1204,7 @@ def create_ui():
 
             with gr.Column(elem_id='ti_gallery_container'):
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
                 ti_progress = gr.HTML(elem_id="ti_progress", value="")
                 ti_outcome = gr.HTML(elem_id="ti_error", value="")
 
@@ -1705,7 +1705,7 @@ def create_ui():
                 if init_field is not None:
                     init_field(saved_value)
 
-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
             apply_field(x, 'visible')
 
         if type(x) == gr.Slider:

modules/ui_common.py

@@ -125,7 +125,7 @@ Requested path was: {f}
 
     with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
         with gr.Group(elem_id=f"{tabname}_gallery_container"):
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
 
         generation_info = None
         with gr.Column():

modules/ui_postprocessing.py

@@ -13,7 +13,7 @@ def create_ui():
                     extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
 
                 with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
-                    image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
+                    image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
 
                 with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
                     extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")

requirements.txt

@@ -5,7 +5,7 @@ basicsr
 fonts
 font-roboto
 gfpgan
-gradio==3.23
+gradio==3.27
 invisible-watermark
 numpy
 omegaconf

requirements_versions.txt

@@ -3,7 +3,7 @@ transformers==4.25.1
 accelerate==0.18.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.23
+gradio==3.27
 numpy==1.23.5
 Pillow==9.4.0
 realesrgan==0.3.0

scripts/custom_code.py

@@ -1,9 +1,40 @@
 import modules.scripts as scripts
 import gradio as gr
+import ast
+import copy
 
 from modules.processing import Processed
 from modules.shared import opts, cmd_opts, state
 
+
+def convertExpr2Expression(expr):
+    expr.lineno = 0
+    expr.col_offset = 0
+    result = ast.Expression(expr.value, lineno=0, col_offset = 0)
+
+    return result
+
+
+def exec_with_return(code, module):
+    """
+    like exec() but can return values
+    https://stackoverflow.com/a/52361938/5862977
+    """
+    code_ast = ast.parse(code)
+
+    init_ast = copy.deepcopy(code_ast)
+    init_ast.body = code_ast.body[:-1]
+
+    last_ast = copy.deepcopy(code_ast)
+    last_ast.body = code_ast.body[-1:]
+
+    exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
+    if type(last_ast.body[0]) == ast.Expr:
+        return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
+    else:
+        exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
+
+
 class Script(scripts.Script):
 
     def title(self):
@@ -13,12 +44,23 @@ class Script(scripts.Script):
         return cmd_opts.allow_code
 
     def ui(self, is_img2img):
-        code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
+        example = """from modules.processing import process_images
 
-        return [code]
+p.width = 768
+p.height = 768
+p.batch_size = 2
+p.steps = 10
+
+return process_images(p)
+"""
 
 
-    def run(self, p, code):
+        code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
+        indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
+
+        return [code, indent_level]
+
+    def run(self, p, code, indent_level):
         assert cmd_opts.allow_code, '--allow-code option must be enabled'
 
         display_result_data = [[], -1, ""]
@@ -29,13 +71,20 @@ class Script(scripts.Script):
             display_result_data[2] = i
 
         from types import ModuleType
-        compiled = compile(code, '', 'exec')
         module = ModuleType("testmodule")
         module.__dict__.update(globals())
         module.p = p
         module.display = display
-        exec(compiled, module.__dict__)
+
+        indent = " " * indent_level
+        indented = code.replace('\n', '\n' + indent)
+        body = f"""def __webuitemp__():
+{indent}{indented}
+__webuitemp__()"""
+
+        result = exec_with_return(body, module)
+
+        if isinstance(result, Processed):
+            return result
 
         return Processed(p, *display_result_data)
-    
-    

scripts/postprocessing_upscale.py

@@ -4,8 +4,8 @@ import numpy as np
 from modules import scripts_postprocessing, shared
 import gradio as gr
 
-from modules.ui_components import FormRow
+from modules.ui_components import FormRow, ToolButton
-
+from modules.ui import switch_values_symbol
 
 upscale_cache = {}
 
@@ -25,9 +25,12 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
 
                     with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
                         with FormRow():
-                            upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
+                            with gr.Column(elem_id="upscaling_column_size", scale=4):
-                            upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
+                                upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+                                upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+                            with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
+                                upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
 
             with FormRow():
                 extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
@@ -36,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
                 extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
                 extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
 
+        upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
         tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
         tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
 

scripts/xyz_grid.py

@@ -374,16 +374,19 @@ class Script(scripts.Script):
                 with gr.Row():
                     x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
                     x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
+                    x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
                     fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
 
                 with gr.Row():
                     y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
                     y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
+                    y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
                     fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
 
                 with gr.Row():
                     z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
                     z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
+                    z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
                     fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
 
         with gr.Row(variant="compact", elem_id="axis_options"):
@@ -401,54 +404,74 @@ class Script(scripts.Script):
             swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
             swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
 
-        def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
+        def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
-            return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
+            return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
 
-        xy_swap_args = [x_type, x_values, y_type, y_values]
+        xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
         swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
-        yz_swap_args = [y_type, y_values, z_type, z_values]
+        yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
         swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
-        xz_swap_args = [x_type, x_values, z_type, z_values]
+        xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
         swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
 
         def fill(x_type):
             axis = self.current_axis_options[x_type]
-            return ", ".join(axis.choices()) if axis.choices else gr.update()
+            return axis.choices() if axis.choices else gr.update()
 
-        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
+        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
-        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
+        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
-        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
+        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
 
-        def select_axis(x_type):
+        def select_axis(axis_type,axis_values_dropdown):
-            return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
+            choices = self.current_axis_options[axis_type].choices
+            has_choices = choices is not None
+            current_values = axis_values_dropdown
+            if has_choices:
+                choices = choices()
+                if isinstance(current_values,str):
+                    current_values = current_values.split(",")
+                current_values = list(filter(lambda x: x in choices, current_values))
+            return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
 
-        x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
+        x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
-        y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
+        y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
-        z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
+        z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
+
+        def get_dropdown_update_from_params(axis,params):
+            val_key = axis + " Values"
+            vals = params.get(val_key,"")
+            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+            return gr.update(value = valslist)
 
         self.infotext_fields = (
             (x_type, "X Type"),
             (x_values, "X Values"),
+            (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
             (y_type, "Y Type"),
             (y_values, "Y Values"),
+            (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
             (z_type, "Z Type"),
             (z_values, "Z Values"),
+            (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
         )
 
-        return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
 
-    def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
+    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
         if not opts.return_grid:
             p.batch_size = 1
 
-        def process_axis(opt, vals):
+        def process_axis(opt, vals, vals_dropdown):
             if opt.label == 'Nothing':
                 return [0]
 
-            valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+            if opt.choices is not None:
+                valslist = vals_dropdown
+            else:
+                valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
 
             if opt.type == int:
                 valslist_ext = []
@@ -506,13 +529,19 @@ class Script(scripts.Script):
             return valslist
 
         x_opt = self.current_axis_options[x_type]
-        xs = process_axis(x_opt, x_values)
+        if x_opt.choices is not None:
+            x_values = ",".join(x_values_dropdown)
+        xs = process_axis(x_opt, x_values, x_values_dropdown)
 
         y_opt = self.current_axis_options[y_type]
-        ys = process_axis(y_opt, y_values)
+        if y_opt.choices is not None:
+            y_values = ",".join(y_values_dropdown)
+        ys = process_axis(y_opt, y_values, y_values_dropdown)
 
         z_opt = self.current_axis_options[z_type]
-        zs = process_axis(z_opt, z_values)
+        if z_opt.choices is not None:
+            z_values = ",".join(z_values_dropdown)
+        zs = process_axis(z_opt, z_values, z_values_dropdown)
 
         # this could be moved to common code, but unlikely to be ever triggered anywhere else
         Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes

style.css

@@ -312,6 +312,10 @@ div.dimensions-tools{
     align-content: center;
 }
 
+div#extras_scale_to_tab div.form{
+    flex-direction: row;
+}
+
 #mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
     height: 480px !important;
     max-height: 480px !important;

webui.py

@@ -69,6 +69,46 @@ else:
     server_name = "0.0.0.0" if cmd_opts.listen else None
 
 
+def fix_asyncio_event_loop_policy():
+    """
+        The default `asyncio` event loop policy only automatically creates
+        event loops in the main threads. Other threads must create event
+        loops explicitly or `asyncio.get_event_loop` (and therefore
+        `.IOLoop.current`) will fail. Installing this policy allows event
+        loops to be created automatically on any thread, matching the
+        behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+    """
+
+    import asyncio
+
+    if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+        # "Any thread" and "selector" should be orthogonal, but there's not a clean
+        # interface for composing policies so pick the right base.
+        _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy  # type: ignore
+    else:
+        _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+    class AnyThreadEventLoopPolicy(_BasePolicy):  # type: ignore
+        """Event loop policy that allows loop creation on any thread.
+        Usage::
+
+            asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+        """
+
+        def get_event_loop(self) -> asyncio.AbstractEventLoop:
+            try:
+                return super().get_event_loop()
+            except (RuntimeError, AssertionError):
+                # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+                # and changed to a RuntimeError in 3.4.3.
+                # "There is no current event loop in thread %r"
+                loop = self.new_event_loop()
+                self.set_event_loop(loop)
+                return loop
+
+    asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
 def check_versions():
     if shared.cmd_opts.skip_version_check:
         return
@@ -101,6 +141,8 @@ Use --skip-version-check commandline argument to disable this check.
 
 
 def initialize():
+    fix_asyncio_event_loop_policy()
+
     check_versions()
 
     extensions.list_extensions()
@@ -128,9 +170,6 @@ def initialize():
     modules.scripts.load_scripts()
     startup_timer.record("load scripts")
 
-    modelloader.load_upscalers()
-    startup_timer.record("load upscalers")
-
     modules.sd_vae.refresh_vae_list()
     startup_timer.record("refresh VAE")