Option to use advanced upscalers with normal img2img

Add option to save before color correction. This helps with some posterisation issues in img2img loopback.

.github/ISSUE_TEMPLATE/bug_report.md

@@ -2,7 +2,7 @@
 name: Bug report
 about: Create a report to help us improve
 title: ''
-labels: bug
+labels: bug-report
 assignees: ''
 
 ---

.gitignore

@@ -6,6 +6,7 @@ __pycache__
 /model.ckpt
 /models/*.ckpt
 /GFPGANv1.3.pth
+/gfpgan/weights/*.pth
 /ui-config.json
 /outputs
 /config.json

javascript/aspectRatioOverlay.js

@@ -18,9 +18,9 @@ function dimensionChange(e,dimname){
 		return;
 	}
 
-	var img2imgMode = gradioApp().querySelector("input[name=radio-img2img_mode]:checked")
+	var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200')
 	if(img2imgMode){
-		img2imgMode=img2imgMode.value
+		img2imgMode=img2imgMode.innerText
 	}else{
 		return;
 	}
@@ -30,12 +30,10 @@ function dimensionChange(e,dimname){
 
 	var targetElement = null;
 
-	if(img2imgMode=='Redraw whole image' && redrawImage){
+	if(img2imgMode=='img2img' && redrawImage){
 		targetElement = redrawImage;
-	}else if(img2imgMode=='Inpaint a part of image' && inpaintImage){
+	}else if(img2imgMode=='Inpaint' && inpaintImage){
 		targetElement = inpaintImage;
-	}else if(img2imgMode=='SD upscale' && redrawImage){
-		targetElement = redrawImage;
 	}
 
 	if(targetElement){
@@ -119,6 +117,3 @@ onUiUpdate(function(){
 		})
 	}
 });
-
-
-

javascript/hints.js

@@ -59,6 +59,7 @@ titles = {
 
     "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_spaces], [width], [height], [sampler], [seed], [model_hash], [prompt_words], [date]; leave empty for default.",
     "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_spaces], [width], [height], [sampler], [seed], [model_hash], [prompt_words], [date]; leave empty for default.",
+    "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",
 
     "Loopback": "Process an image, use it as an input, repeat.",
     "Loops": "How many times to repeat processing an image and using it as input for the next iteration",

javascript/imageMaskFix.js

@@ -0,0 +1,45 @@
+/**
+ * temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
+ * @see https://github.com/gradio-app/gradio/issues/1721
+ */
+window.addEventListener( 'resize', () => imageMaskResize());
+function imageMaskResize() {
+    const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
+    if ( ! canvases.length ) {
+    canvases_fixed = false;
+    window.removeEventListener( 'resize', imageMaskResize );
+    return;
+    }
+
+    const wrapper = canvases[0].closest('.touch-none');
+    const previewImage = wrapper.previousElementSibling;
+
+    if ( ! previewImage.complete ) {
+        previewImage.addEventListener( 'load', () => imageMaskResize());
+        return;
+    }
+
+    const w = previewImage.width;
+    const h = previewImage.height;
+    const nw = previewImage.naturalWidth;
+    const nh = previewImage.naturalHeight;
+    const portrait = nh > nw;
+    const factor = portrait;
+
+    const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
+    const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
+
+    wrapper.style.width = `${wW}px`;
+    wrapper.style.height = `${wH}px`;
+    wrapper.style.left = `${(w-wW)/2}px`;
+    wrapper.style.top = `${(h-wH)/2}px`;
+
+    canvases.forEach( c => {
+        c.style.width = c.style.height = '';
+        c.style.maxWidth = '100%';
+        c.style.maxHeight = '100%';
+        c.style.objectFit = 'contain';
+    });
+ }
+  
+ onUiUpdate(() => imageMaskResize());

javascript/notification.js

@@ -2,7 +2,19 @@
 
 let lastHeadImg = null;
 
+notificationButton = null
+
 onUiUpdate(function(){
+    if(notificationButton == null){
+        notificationButton = gradioApp().getElementById('request_notifications')
+
+        if(notificationButton != null){
+            notificationButton.addEventListener('click', function (evt) {
+                Notification.requestPermission();
+            },true);
+        }
+    }
+
     const galleryPreviews = gradioApp().querySelectorAll('img.h-full.w-full.overflow-hidden');
 
     if (galleryPreviews == null) return;

javascript/progressbar.js

@@ -3,7 +3,7 @@ global_progressbar = null
 
 onUiUpdate(function(){
     progressbar = gradioApp().getElementById('progressbar')
-    progressDiv = gradioApp().querySelectorAll('.progressDiv').length > 0;
+    progressDiv = gradioApp().querySelectorAll('#progressSpan').length > 0;
     interrupt = gradioApp().getElementById('interrupt')
 	if(progressbar!= null && progressbar != global_progressbar){
 	    global_progressbar = progressbar
@@ -40,7 +40,7 @@ function requestMoreProgress(){
     if(btn==null) return;
 
     btn.click();
-    progressDiv = gradioApp().querySelectorAll('.progressDiv').length > 0;
+    progressDiv = gradioApp().querySelectorAll('#progressSpan').length > 0;
     if(progressDiv){
         interrupt.style.display = "block"
     }

modules/gfpgan_model.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import traceback
+from glob import glob
 
 from modules import shared, devices
 from modules.shared import cmd_opts
@@ -11,14 +12,20 @@ import modules.face_restoration
 def gfpgan_model_path():
     from modules.shared import cmd_opts
 
+    filemask = 'GFPGAN*.pth'
+
+    if cmd_opts.gfpgan_model is not None:
+        return cmd_opts.gfpgan_model
+
     places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
-    files = [cmd_opts.gfpgan_model] + [os.path.join(dirname, cmd_opts.gfpgan_model) for dirname in places]
-    found = [x for x in files if os.path.exists(x)]
 
-    if len(found) == 0:
-        raise Exception("GFPGAN model not found in paths: " + ", ".join(files))
+    filename = None
+    for place in places:
+        filename = next(iter(glob(os.path.join(place, filemask))), None)
+        if filename is not None:
+            break
 
-    return found[0]
+    return filename
 
 
 loaded_gfpgan_model = None
@@ -34,7 +41,7 @@ def gfpgan():
     if gfpgan_constructor is None:
         return None
 
-    model = gfpgan_constructor(model_path=gfpgan_model_path(), upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
+    model = gfpgan_constructor(model_path=gfpgan_model_path() or 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth', upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
     model.gfpgan.to(shared.device)
     loaded_gfpgan_model = model
 

modules/images.py

@@ -209,8 +209,16 @@ def draw_prompt_matrix(im, width, height, all_prompts):
 
 
 def resize_image(resize_mode, im, width, height):
+    def resize(im, w, h):
+        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
+            return im.resize((w, h), resample=LANCZOS)
+
+        upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
+        return upscaler.upscale(im, w, h)
+
     if resize_mode == 0:
-        res = im.resize((width, height), resample=LANCZOS)
+        res = resize(im, width, height)
+
     elif resize_mode == 1:
         ratio = width / height
         src_ratio = im.width / im.height
@@ -218,9 +226,10 @@ def resize_image(resize_mode, im, width, height):
         src_w = width if ratio > src_ratio else im.width * height // im.height
         src_h = height if ratio <= src_ratio else im.height * width // im.width
 
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
+        resized = resize(im, src_w, src_h)
         res = Image.new("RGB", (width, height))
         res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
+
     else:
         ratio = width / height
         src_ratio = im.width / im.height
@@ -228,7 +237,7 @@ def resize_image(resize_mode, im, width, height):
         src_w = width if ratio < src_ratio else im.width * height // im.height
         src_h = height if ratio >= src_ratio else im.height * width // im.width
 
-        resized = im.resize((src_w, src_h), resample=LANCZOS)
+        resized = resize(im, src_w, src_h)
         res = Image.new("RGB", (width, height))
         res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2))
 
@@ -249,7 +258,6 @@ invalid_filename_prefix = ' '
 invalid_filename_postfix = ' .'
 re_nonletters = re.compile(r'[\s'+string.punctuation+']+')
 max_filename_part_length = 128
-max_prompt_words = 8
 
 
 def sanitize_filename_part(text, replace_spaces=True):
@@ -263,6 +271,8 @@ def sanitize_filename_part(text, replace_spaces=True):
 
 
 def apply_filename_pattern(x, p, seed, prompt):
+    max_prompt_words = opts.directories_max_prompt_words
+
     if seed is not None:
         x = x.replace("[seed]", str(seed))
 
@@ -311,7 +321,7 @@ def get_next_sequence_number(path, basename):
 
     return result + 1
 
-def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None):
+def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
     if short_filename or prompt is None or seed is None:
         file_decoration = ""
     elif opts.save_to_dirs:
@@ -322,7 +332,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     if file_decoration != "":
         file_decoration = "-" + file_decoration.lower()
 
-    file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt)
+    file_decoration = apply_filename_pattern(file_decoration, p, seed, prompt) + suffix
 
     if extension == 'png' and opts.enable_pnginfo and info is not None:
         pnginfo = PngImagePlugin.PngInfo()

modules/img2img.py

@@ -118,4 +118,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
 
     shared.total_tqdm.clear()
 
-    return processed.images, processed.js(), plaintext_to_html(processed.info)
+    generation_info_js = processed.js()
+    if opts.samples_log_stdout:
+        print(generation_info_js)
+
+    return processed.images, generation_info_js, plaintext_to_html(processed.info)

modules/processing.py

@@ -20,6 +20,7 @@ import modules.shared as shared
 import modules.face_restoration
 import modules.images as images
 import modules.styles
+import logging
 
 
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
@@ -28,11 +29,13 @@ opt_f = 8
 
 
 def setup_color_correction(image):
+    logging.info("Calibrating color correction.")
     correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
     return correction_target
 
 
 def apply_color_correction(correction, image):
+    logging.info("Applying color correction.")
     image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
         cv2.cvtColor(
             np.asarray(image),
@@ -357,7 +360,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
                 if p.restore_faces:
                     if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
-                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
+                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
 
                     devices.torch_gc()
 
@@ -366,6 +369,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 image = Image.fromarray(x_sample)
 
                 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 = apply_color_correction(p.color_corrections[i], image)
 
                 if p.overlay_images is not None and i < len(p.overlay_images):
@@ -457,7 +462,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         else:
             decoded_samples = self.sd_model.decode_first_stage(samples)
 
-            if opts.upscaler_for_hires_fix is None or opts.upscaler_for_hires_fix == "None":
+            if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
                 decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
             else:
                 lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -467,7 +472,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                     x_sample = x_sample.astype(np.uint8)
                     image = Image.fromarray(x_sample)
-                    upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_hires_fix][0]
+                    upscaler = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img][0]
                     image = upscaler.upscale(image, self.width, self.height)
                     image = np.array(image).astype(np.float32) / 255.0
                     image = np.moveaxis(image, 2, 0)

modules/shared.py

@@ -2,7 +2,6 @@ import sys
 import argparse
 import json
 import os
-from glob import glob
 import gradio as gr
 import tqdm
 
@@ -22,7 +21,7 @@ parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; this checkpoint will be added to the list of checkpoints and loaded by default if you don't have a checkpoint selected in settings",)
 parser.add_argument("--ckpt-dir", type=str, default=os.path.join(script_path, 'models'), help="path to directory with stable diffusion checkpoints",)
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=next(iter(glob('GFPGAN*.pth')), ''))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
 parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
 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")
@@ -100,80 +99,127 @@ def realesrgan_models_names():
     return [x.name for x in modules.realesrgan_model.get_realesrgan_models()]
 
 
-class Options:
-    class OptionInfo:
-        def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
-            self.default = default
-            self.label = label
-            self.component = component
-            self.component_args = component_args
-            self.onchange = onchange
+class OptionInfo:
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None):
+        self.default = default
+        self.label = label
+        self.component = component
+        self.component_args = component_args
+        self.onchange = onchange
+        self.section = None
 
+
+def options_section(section_identifer, options_dict):
+    for k, v in options_dict.items():
+        v.section = section_identifer
+
+    return options_dict
+
+
+hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
+
+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"),
+
+    "grid_save": OptionInfo(True, "Always save all generated image grids"),
+    "grid_format": OptionInfo('png', 'File format for grids'),
+    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
+    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+
+    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
+    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
+    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
+    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
+
+    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+}))
+
+options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
+    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
+    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
+    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
+    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
+    "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),
+}))
+
+options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
+    "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
+    "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
+    "directories_max_prompt_words": OptionInfo(8, "Max prompt words", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1}),
+}))
+
+options_templates.update(options_section(('upscaling', "Upscaling"), {
+    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "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(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+    "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
+    "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
+    "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
+    "ldsr_pre_down": OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
+    "ldsr_post_down": OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
+
+    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
+}))
+
+options_templates.update(options_section(('face-restoration', "Face restoration"), {
+    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
+    "save_selected_only": OptionInfo(False, "When using 'Save' button, only save a single selected image"),
+}))
+
+options_templates.update(options_section(('system', "System"), {
+    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
+    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
+}))
+
+options_templates.update(options_section(('sd', "Stable Diffusion"), {
+    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
+    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),    
+    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
+    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
+    "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
+    "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
+    "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
+}))
+
+options_templates.update(options_section(('interrogate', "Interrogate Options"), {
+    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
+    "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
+    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+    "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+    "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
+}))
+
+options_templates.update(options_section(('ui', "User interface"), {
+    "show_progressbar": OptionInfo(True, "Show progressbar"),
+    "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
+    "font": OptionInfo("", "Font for image grids that have text"),
+    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
+    "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+}))
+
+
+class Options:
     data = None
-    hide_dirs = {"visible": False} if cmd_opts.hide_ui_dir_config else None
-    data_labels = {
-        "samples_filename_pattern": OptionInfo("", "Images filename pattern"),
-        "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-        "grid_save_to_dirs": OptionInfo(False, "Save grids to subdirectory"),
-        "directories_filename_pattern": OptionInfo("", "Directory name pattern"),
-        "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to two directories below", component_args=hide_dirs),
-        "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
-        "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
-        "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
-        "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
-        "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),
-        "samples_save": OptionInfo(True, "Always save all generated images"),
-        "save_selected_only": OptionInfo(False, "When using 'Save' button, only save a single selected image"),
-        "samples_format": OptionInfo('png', 'File format for individual samples'),
-        "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
-        "grid_save": OptionInfo(True, "Always save all generated image grids"),
-        "return_grid": OptionInfo(True, "Show grid in results for web"),
-        "grid_format": OptionInfo('png', 'File format for grids'),
-        "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
-        "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
-        "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
-        "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-        "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
-        "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
-        "add_model_hash_to_info": OptionInfo(False, "Add model hash to generation information"),
-        "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
-        "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
-        "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
-        "font": OptionInfo("", "Font for image grids that have text"),
-        "enable_emphasis": OptionInfo(True, "Use (text) to make model pay more attention to text and [text] to make it pay less attention"),
-        "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-        "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
-        "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "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(["Real-ESRGAN 4x plus", "Real-ESRGAN 4x plus anime 6B"], "Select which RealESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
-        "SWIN_tile": OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}),
-        "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-        "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
-        "ldsr_pre_down":OptionInfo(1, "LDSR Pre-process downssample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
-        "ldsr_post_down":OptionInfo(1, "LDSR Post-process down-sample scale. 1 = no down-sampling, 4 = 1/4 scale.", gr.Slider, {"minimum": 1, "maximum": 4, "step": 1}),
-        "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
-        "upscaler_for_hires_fix": OptionInfo(None, "Upscaler for highres. fix", gr.Radio, lambda: {"choices": [x.name for x in sd_upscalers]}),
-        "show_progressbar": OptionInfo(True, "Show progressbar"),
-        "show_progress_every_n_steps": OptionInfo(0, "Show show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
-        "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
-        "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step":1}),
-        "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
-        "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-        "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
-        "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-        "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
-        "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
-        "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-        "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-        "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-        "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
-        "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
-        "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
-        "js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
-        "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process"),
-    }
+    data_labels = options_templates
+    typemap = {int: float}
 
     def __init__(self):
         self.data = {k: v.default for k, v in self.data_labels.items()}
@@ -199,10 +245,29 @@ class Options:
         with open(filename, "w", encoding="utf8") as file:
             json.dump(self.data, file)
 
+    def same_type(self, x, y):
+        if x is None or y is None:
+            return True
+
+        type_x = self.typemap.get(type(x), type(x))
+        type_y = self.typemap.get(type(y), type(y))
+
+        return type_x == type_y
+
     def load(self, filename):
         with open(filename, "r", encoding="utf8") as file:
             self.data = json.load(file)
 
+        bad_settings = 0
+        for k, v in self.data.items():
+            info = self.data_labels.get(k, None)
+            if info is not None and not self.same_type(info.default, v):
+                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
+                bad_settings += 1
+
+        if bad_settings > 0:
+            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
+
     def onchange(self, key, func):
         item = self.data_labels.get(key)
         item.onchange = func

modules/txt2img.py

@@ -37,12 +37,14 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
     processed = modules.scripts.scripts_txt2img.run(p, *args)
 
-    if processed is not None:
-        pass
-    else:
+    if processed is None:
         processed = process_images(p)
 
     shared.total_tqdm.clear()
 
-    return processed.images, processed.js(), plaintext_to_html(processed.info)
+    generation_info_js = processed.js()
+    if opts.samples_log_stdout:
+        print(generation_info_js)
+
+    return processed.images, generation_info_js, plaintext_to_html(processed.info)
 

modules/ui.py

@@ -22,7 +22,6 @@ from modules.paths import script_path
 from modules.shared import opts, cmd_opts
 import modules.shared as shared
 from modules.sd_samplers import samplers, samplers_for_img2img
-import modules.realesrgan_model as realesrgan
 import modules.ldsr_model
 import modules.scripts
 import modules.gfpgan_model
@@ -201,7 +200,7 @@ def check_progress_call():
         else:
             preview_visibility = gr_show(True)
 
-    return f"<span style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
+    return f"<span id='progressSpan' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image
 
 
 def check_progress_call_initial():
@@ -435,7 +434,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
                 with gr.Group():
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                    txt2img_gallery = gr.Gallery(label='Output', elem_id='txt2img_gallery').style(grid=4)
+                    txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
 
                 with gr.Group():
                     with gr.Row():
@@ -590,7 +589,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
 
                 with gr.Group():
                     img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                    img2img_gallery = gr.Gallery(label='Output', elem_id='img2img_gallery').style(grid=4)
+                    img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
 
                 with gr.Group():
                     with gr.Row():
@@ -750,9 +749,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
 
             with gr.Column(variant='panel'):
-                result_images = gr.Gallery(label="Result")
+                result_images = gr.Gallery(label="Result", show_label=False)
                 html_info_x = gr.HTML()
                 html_info = gr.HTML()
+                extras_send_to_img2img = gr.Button('Send to img2img')
+                extras_send_to_inpaint = gr.Button('Send to inpaint')
 
         submit.click(
             fn=run_extras,
@@ -775,6 +776,20 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 html_info,
             ]
         )
+     
+        extras_send_to_img2img.click(
+            fn=lambda x: image_from_url_text(x),
+            _js="extract_image_from_gallery_img2img",
+            inputs=[result_images],
+            outputs=[init_img],
+        )
+        
+        extras_send_to_inpaint.click(
+            fn=lambda x: image_from_url_text(x),
+            _js="extract_image_from_gallery_img2img",
+            inputs=[result_images],
+            outputs=[init_img_with_mask],
+        )
 
     pnginfo_interface = gr.Interface(
         wrap_gradio_call(run_pnginfo),
@@ -814,12 +829,13 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
         return comp(label=info.label, value=fun, **(args or {}))
 
     components = []
-    keys = list(opts.data_labels.keys())
-    settings_cols = 3
-    items_per_col = math.ceil(len(keys) / settings_cols)
 
     def run_settings(*args):
-        up = []
+        changed = 0
+
+        for key, value, comp in zip(opts.data_labels.keys(), args, components):
+            if not opts.same_type(value, opts.data_labels[key].default):
+                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             comp_args = opts.data_labels[key].component_args
@@ -829,27 +845,58 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             oldval = opts.data.get(key, None)
             opts.data[key] = value
 
-            if oldval != value and opts.data_labels[key].onchange is not None:
-                opts.data_labels[key].onchange()
+            if oldval != value:
+                if opts.data_labels[key].onchange is not None:
+                    opts.data_labels[key].onchange()
 
-            up.append(comp.update(value=value))
+                changed += 1
 
         opts.save(shared.config_filename)
 
-        return 'Settings applied.'
+        return f'{changed} settings changed.'
 
     with gr.Blocks(analytics_enabled=False) as settings_interface:
         settings_submit = gr.Button(value="Apply settings", variant='primary')
         result = gr.HTML()
 
-        with gr.Row(elem_id="settings").style(equal_height=False):
-            for colno in range(settings_cols):
-                with gr.Column(variant='panel'):
-                    for rowno in range(items_per_col):
-                        index = rowno + colno * items_per_col
+        settings_cols = 3
+        items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
 
-                        if index < len(keys):
-                            components.append(create_setting_component(keys[index]))
+        cols_displayed = 0
+        items_displayed = 0
+        previous_section = None
+        column = None
+        with gr.Row(elem_id="settings").style(equal_height=False):
+            for i, (k, item) in enumerate(opts.data_labels.items()):
+
+                if previous_section != item.section:
+                    if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
+                        if column is not None:
+                            column.__exit__()
+
+                        column = gr.Column(variant='panel')
+                        column.__enter__()
+
+                        items_displayed = 0
+                        cols_displayed += 1
+
+                    previous_section = item.section
+
+                    gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
+
+                components.append(create_setting_component(k))
+                items_displayed += 1
+
+        request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
+        request_notifications.click(
+            fn=lambda: None,
+            inputs=[],
+            outputs=[],
+            _js='function(){}'
+        )
+
+        if column is not None:
+            column.__exit__()
 
         settings_submit.click(
             fn=run_settings,
@@ -857,14 +904,6 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             outputs=[result]
         )
 
-        request_notifications = gr.Button(value='Request browser notifications')
-        request_notifications.click(
-            fn=lambda: None,
-            inputs=[],
-            outputs=[],
-            _js='() => Notification.requestPermission()'
-        )
-
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
@@ -1001,7 +1040,7 @@ with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfil
     javascript = f'<script>{jsfile.read()}</script>'
 
 jsdir = os.path.join(script_path, "javascript")
-for filename in os.listdir(jsdir):
+for filename in sorted(os.listdir(jsdir)):
     with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
         javascript += f"\n<script>{jsfile.read()}</script>"
 

scripts/outpainting_mk_2.py

@@ -11,7 +11,7 @@ from modules import images, processing, devices
 from modules.processing import Processed, process_images
 from modules.shared import opts, cmd_opts, state
 
-
+#  https://github.com/parlance-zz/g-diffuser-bot
 def expand(x, dir, amount, power=0.75):
     is_left = dir == 3
     is_right = dir == 1

webui.py

@@ -53,6 +53,7 @@ def wrap_gradio_gpu_call(func):
         shared.state.current_latent = None
         shared.state.current_image = None
         shared.state.current_image_sampling_step = 0
+        shared.state.interrupted = False
 
         with queue_lock:
             res = func(*args, **kwargs)