import sys import os cwd = os.getcwd() utils_dir = os.path.join(cwd, 'extensions', 'batch-face-swap', 'scripts') sys.path.extend([utils_dir]) from bfs_utils import * from face_detect import * from sd_helpers import renderImg2Img, renderTxt2Img import modules.scripts as scripts import gradio as gr import time import random from modules import images, masking, generation_parameters_copypaste, script_callbacks from modules.processing import process_images, create_infotext, Processed from modules.shared import opts, cmd_opts, state import modules.shared as shared import modules.sd_samplers import cv2 import numpy as np from PIL import Image, ImageOps, ImageDraw, ImageFilter, UnidentifiedImageError import math def apply_checkpoint(x): info = modules.sd_models.get_closet_checkpoint_match(x) if info is None: raise RuntimeError(f"Unknown checkpoint: {x}") modules.sd_models.reload_model_weights(shared.sd_model, info) def findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskWidth, maskHeight, skip): rejected = 0 masks = [] faces_info = [] imageOriginal = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) heightOriginal = height widthOriginal = width # Calculate the size of each small image small_width = width if onlyHorizontal else math.ceil(width / divider) small_height = height if onlyVertical else math.ceil(height / divider) # Divide the large image into a list of small images small_images = [] for i in range(0, height, small_height): for j in range(0, width, small_width): small_images.append(image.crop((j, i, j + small_width, i + small_height))) # Process each small image processed_images = [] facesInImage = 0 for i, small_image in enumerate(small_images): small_image_index = i small_image = cv2.cvtColor(np.array(small_image), cv2.COLOR_RGB2BGR) faces = [] if facecfg.faceMode == FaceMode.ORIGINAL: landmarks = [] landmarks = getFacialLandmarks(small_image, facecfg) numberOfFaces = int(len(landmarks)) totalNumberOfFaces += numberOfFaces if countFaces: continue faces = [] for landmark in landmarks: face_info = {} convexhull = cv2.convexHull(landmark) faces.append(convexhull) faces_info.append(computeFaceInfo(landmark, onlyHorizontal, divider, small_width, small_height, small_image_index)) elif facecfg.faceMode == FaceMode.YUNET: known_face_rects = [] # first find the faces the old way, since OpenCV is BAD at faces near the camera # save the convex hulls, but also getting bounding boxes so OpenCV can skip those landmarks = getFacialLandmarks(small_image, facecfg) for landmark in landmarks: face_info = {} convexhull = cv2.convexHull(landmark) faces.append(convexhull) bounds = cv2.boundingRect(convexhull) known_face_rects.append(list(bounds)) # convert tuple to array for consistency faces_info.append(computeFaceInfo(landmark, onlyHorizontal, divider, small_width, small_height, small_image_index)) faceRects = getFaceRectanglesYuNet(small_image, known_face_rects) for rect in faceRects: landmarkHull, face_info = getFacialLandmarkConvexHull(image, rect, onlyHorizontal, divider, small_width, small_height, small_image_index, facecfg) if landmarkHull is not None: faces.append(landmarkHull) faces_info.append(face_info) else: rejected += 1 numberOfFaces = int(len(faces)) totalNumberOfFaces += numberOfFaces if countFaces: continue else: # use OpenCV2 multi-scale face detector to find all the faces known_face_rects = [] # first find the faces the old way, since OpenCV is BAD at faces near the camera # save the convex hulls, but also getting bounding boxes so OpenCV can skip those landmarks = getFacialLandmarks(small_image, facecfg) for landmark in landmarks: face_info = {} convexhull = cv2.convexHull(landmark) faces.append(convexhull) bounds = cv2.boundingRect(convexhull) known_face_rects.append(list(bounds)) # convert tuple to array for consistency faces_info.append(computeFaceInfo(landmark, onlyHorizontal, divider, small_width, small_height, small_image_index)) faceRects = getFaceRectangles(small_image, known_face_rects, facecfg) for rect in faceRects: landmarkHull, face_info = getFacialLandmarkConvexHull(image, rect, onlyHorizontal, divider, small_width, small_height, small_image_index, facecfg) if landmarkHull is not None: faces.append(landmarkHull) faces_info.append(face_info) else: rejected += 1 numberOfFaces = int(len(faces)) totalNumberOfFaces += numberOfFaces if countFaces: continue if len(faces) == 0: small_image[:] = (0, 0, 0) if numberOfFaces > 0: facesInImage += numberOfFaces if facesInImage == 0 and i == len(small_images) - 1: skip = 1 for i in range(len(faces)): processed_images = [] for k in range(len(small_images)): mask = np.zeros((small_height, small_width), np.uint8) if k == small_image_index: small_image = cv2.fillConvexPoly(mask, faces[i], 255) processed_image = Image.fromarray(small_image) processed_images.append(processed_image) else: processed_image = Image.fromarray(mask) processed_images.append(processed_image) # Create a new image with the same size as the original large image new_image = Image.new('RGB', (width, height)) # Paste the processed small images into the new image if onlyHorizontal == True: for i, processed_image in enumerate(processed_images): x = (i // divider) * small_width y = (i % divider) * small_height new_image.paste(processed_image, (x, y)) else: for i, processed_image in enumerate(processed_images): x = (i % divider) * small_width y = (i // divider) * small_height new_image.paste(processed_image, (x, y)) masks.append(new_image) if countFaces: return totalNumberOfFaces if file != None: if FaceDetectDevelopment: print(f"Found {facesInImage} face(s) in {str(file)} (rejected {rejected} from OpenCV)") else: print(f"Found {facesInImage} face(s) in {str(file)}") # else: # print(f"Found {facesInImage} face(s)") binary_masks = [] for i, mask in enumerate(masks): gray_image = mask.convert('L') numpy_array = np.array(gray_image) binary_mask = cv2.threshold(numpy_array, 200, 255, cv2.THRESH_BINARY)[1] if maskWidth != 100 or maskHeight != 100: binary_mask = maskResize(binary_mask, maskWidth, maskHeight) binary_masks.append(binary_mask) # try: # kernel = np.ones((int(math.ceil(0.011*height)),int(math.ceil(0.011*height))),'uint8') # dilated = cv2.dilate(binary_mask,kernel,iterations=1) # kernel = np.ones((int(math.ceil(0.0045*height)),int(math.ceil(0.0025*height))),'uint8') # dilated = cv2.dilate(dilated,kernel,iterations=1,anchor=(1, -1)) # kernel = np.ones((int(math.ceil(0.014*height)),int(math.ceil(0.0025*height))),'uint8') # dilated = cv2.dilate(dilated,kernel,iterations=1,anchor=(-1, 1)) # mask = dilated # except cv2.error: # mask = dilated if singleMaskPerImage and len(binary_masks) > 0: result = [] h, w = binary_masks[0].shape result = np.full((h,w), 0, dtype=np.uint8) for mask in binary_masks: result = cv2.add(result, mask) masks = [ result ] return masks, totalNumberOfFaces, faces_info, skip else: masks = binary_masks return masks, totalNumberOfFaces, faces_info, skip # generate debug image def faceDebug(p, masks, image, finishedImages, invertMask, forced_filename, output_path, info): generatedImages = [] paste_to = [] imageOriginal = image overlay_image = image for n, mask in enumerate(masks): mask = Image.fromarray(masks[n]) if invertMask: mask = ImageOps.invert(mask) image_masked = Image.new('RGBa', (image.width, image.height)) image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L'))) overlay_image = image_masked.convert('RGBA') debugsave(overlay_image) def faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, output_path, info, selectedTab,mainTab, geninfo, faces_info, rotation_threshold, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height): bfs_prompt = bfs_prompt if overridePrompt else p.prompt bfs_nprompt = bfs_nprompt if overridePrompt else p.negative_prompt original_model = modules.sd_models.select_checkpoint() if overrideModel: apply_checkpoint(sd_model) sd_sampler = sd_sampler if overrideSampler else p.sampler_name batch_size = 1 if mainTab == "txt2img" else p.batch_size n_iter = 1 if mainTab == "txt2img" else p.n_iter denoising_strength = 0.5 if overrideDenoising else denoising_strength seed = int(random.randrange(4294967294)) if overrideSeed else p.seed steps = steps if overrideSteps else p.steps cfg_scale = cfg_scale if overrideCfgScale else p.cfg_scale bfs_width = bfs_width if overrideSize else p.width bfs_height = bfs_height if overrideSize else p.height # automatically adjust mask_blur based on the size of the image but don't make it higher than 30 mask_blur = np.clip(int(math.ceil(0.01*image.height if image.height > image.width else 0.01*image.width)), None, 30) if overrideMaskBlur else mask_blur # automatically adjust inpaint_full_res_padding based on the size of the image inpaint_full_res_padding = int(math.ceil(0.03*image.height if image.height > image.width else 0.03*image.width)) if overridePadding else inpaint_full_res_padding inpainting_full_res = 1 inpainting_fill = 1 wasGrid = p.do_not_save_grid p.do_not_save_grid = True p.do_not_save_samples = True index = 0 generatedImages = [] paste_to = [] imageOriginal = image overlay_image = image for n, mask in enumerate(masks): rotate = False mask = Image.fromarray(masks[n]) if invertMask: image_mask = ImageOps.invert(mask) else: image_masked = Image.new('RGBa', (image.width, image.height)) image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(mask.convert('L'))) overlay_image = image_masked.convert('RGBA') crop_region = masking.get_crop_region(np.array(mask), inpaint_full_res_padding) crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height) x1, y1, x2, y2 = crop_region paste_to.append((x1, y1, x2-x1, y2-y1)) for i in range(len(faces_info)): try: pixel_color = mask.getpixel((faces_info[i]["center"][0],faces_info[i]["center"][1])) except IndexError: pixel_color = 0 if pixel_color == 255: index = i break mask = mask.crop(crop_region) image_mask = images.resize_image(2, mask, p.width, p.height) image = image.crop(crop_region) image = images.resize_image(2, image, p.width, p.height) image_cropped = image rotation_threshold = rotation_threshold if 90+rotation_threshold > faces_info[index]["angle"] and 90-rotation_threshold < faces_info[index]["angle"]: pass else: angle_difference = (90-int(faces_info[index]["angle"]) + 360) % 360 image = image.rotate(angle_difference, expand=True) image_mask = image_mask.rotate(angle_difference, expand=True) rotate = True if geninfo != "": bfs_prompt = str(geninfo.get("Prompt")) bfs_nprompt = str(geninfo.get("Negative prompt")) sd_sampler = str(geninfo.get("Sampler")) cfg_scale = float(geninfo.get("CFG scale")) bfs_width = int(geninfo.get("Size-1")) bfs_height = int(geninfo.get("Size-2")) proc = renderImg2Img( bfs_prompt, bfs_nprompt, sd_sampler, steps, cfg_scale, seed, bfs_width, bfs_height, image, image_mask, batch_size, n_iter, denoising_strength, mask_blur, inpainting_fill, inpainting_full_res, inpaint_full_res_padding, do_not_save_samples = True, ) apply_checkpoint(original_model.title) if rotate: for i in range(len(proc.images)): image_copy = image_cropped.copy() proc.images[i] = proc.images[i].rotate(int(faces_info[index]["angle"])-90) w1, h1 = image_cropped.size w2, h2 = proc.images[i].size x = (w1 - w2) // 2 y = (h1 - h2) // 2 image_copy.paste(proc.images[i], (x, y)) proc.images[i] = image_copy generatedImages.append(proc.images) image = imageOriginal for j in range(n_iter * batch_size): if not invertMask: image = imageOriginal for k in range(len(generatedImages)): mask = Image.fromarray(masks[k]) mask = mask.filter(ImageFilter.GaussianBlur(mask_blur)) image = apply_overlay(generatedImages[k][j], paste_to[k], image, mask) else: image = proc.images[j] info = infotext(p) final_forced_filename = forced_filename+"_"+str(j+1) if forced_filename != None and (batch_size > 1 or n_iter > 1) else forced_filename if opts.samples_format != "png" and image.mode != 'RGB': image = image.convert('RGB') images.save_image(image, output_path if output_path !="" else opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=final_forced_filename) finishedImages.append(image) p.do_not_save_samples = False p.do_not_save_grid = wasGrid return finishedImages def generateImages(p, facecfg, input_image, input_path, searchSubdir, viewResults, divider, howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab,mainTab, loadGenParams, rotation_threshold): suffix = '' info = infotext(p) if selectedTab == "generateMasksTab": finishedImages = [] wasCountFaces = False totalNumberOfFaces = 0 allFiles = [] geninfo = "" onlyHorizontal = ("Horizontal" in howSplit) onlyVertical = ("Vertical" in howSplit) # if neither path nor image, we're done if input_path == '' and input_image is None: return finishedImages # flag whether we're processing a directory or a specified image # (the code after this supports multiple images in an array, but the UI only allows a single image) usingFilenames = (input_path != '') if usingFilenames: allFiles = listFiles(input_path, searchSubdir, allFiles) else: allFiles += input_image start_time = time.thread_time() if countFaces: print("\nCounting faces...") for i, file in enumerate(allFiles): skip = 0 image = Image.open(file) if usingFilenames else file width, height = image.size masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskWidth, maskHeight, skip) if not onlyMask and countFaces: print(f"\nWill process {len(allFiles)} images, found {totalNumberOfFaces} faces, generating {p.n_iter * p.batch_size} new images for each.") state.job_count = totalNumberOfFaces * p.n_iter elif not onlyMask and not countFaces: print(f"\nWill process {len(allFiles)} images, generating {p.n_iter * p.batch_size} new images for each.") state.job_count = len(allFiles) * p.n_iter for i, file in enumerate(allFiles): if usingFilenames and keepOriginalName: forced_filename = os.path.splitext(os.path.basename(file))[0] else: forced_filename = None if usingFilenames and saveToOriginalFolder: output_path = os.path.dirname(file) if countFaces: state.job = f"{i+1} out of {totalNumberOfFaces}" totalNumberOfFaces = 0 wasCountFaces = True countFaces = False else: state.job = f"{i+1} out of {len(allFiles)}" if state.skipped: state.skipped = False if state.interrupted and onlyMask: state.interrupted = False elif state.interrupted: break try: image = Image.open(file) if usingFilenames else file width, height = image.size if loadGenParams: geninfo, _ = read_info_from_image(image) geninfo = generation_parameters_copypaste.parse_generation_parameters(geninfo) except UnidentifiedImageError: print(f"\nUnable to open {file}, skipping") continue skip = 0 masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskWidth, maskHeight, skip) if facecfg.debugSave: faceDebug(p, masks, image, finishedImages, invertMask, forced_filename, output_path, info) # Only generate mask if onlyMask: suffix = '_mask' # Load mask for i, mask in enumerate(masks): mask = Image.fromarray(mask) # Invert mask if needed if invertMask: mask = ImageOps.invert(mask) finishedImages.append(mask) if saveMask and skip != 1: custom_save_image(p, mask, output_path, forced_filename, suffix, info) elif saveMask and skip == 1 and saveNoFace: custom_save_image(p, mask, output_path, forced_filename, suffix, info) # If face was not found but user wants to save images without face if skip == 1 and saveNoFace and not onlyMask: custom_save_image(p, image, output_path, forced_filename, suffix, info) finishedImages.append(image) state.skipped = True continue # If face was not found, just skip if skip == 1: state.skipped = True continue if not onlyMask: finishedImages = faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, output_path, info, selectedTab, mainTab, geninfo, faces_info, rotation_threshold, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height,) if usingFilenames and not viewResults: finishedImages = [] if wasCountFaces == True: countFaces = True timing = time.thread_time() - start_time print(f"Found {totalNumberOfFaces} faces in {len(allFiles)} images in {timing} seconds.") # EXISTING MASKS elif selectedTab == "existingMasksTab": finishedImages = [] allImages = [] allMasks = [] searchSubdir = False if pathExisting != '' and pathMasksExisting != '': allImages = listFiles(pathExisting, searchSubdir, allImages) allMasks = listFiles(pathMasksExisting, searchSubdir, allMasks) print(f"\nWill process {len(allImages)} images, generating {p.n_iter * p.batch_size} new images for each.") state.job_count = len(allImages) * p.n_iter for i, file in enumerate(allImages): forced_filename = os.path.splitext(os.path.basename(file))[0] state.job = f"{i+1} out of {len(allImages)}" if state.skipped: state.skipped = False elif state.interrupted: break try: image = Image.open(file) width, height = image.size masks = [] masks.append(Image.open(os.path.join(pathMasksExisting, os.path.splitext(os.path.basename(file))[0])+os.path.splitext(allMasks[i])[1])) except UnidentifiedImageError: print(f"\nUnable to open {file}, skipping") continue finishedImages = faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, output_pathExisting, info, selectedTab, mainTab, faces_info, rotation_threshold, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height,) if not viewResults: finishedImages = [] return finishedImages class Script(scripts.Script): def title(self): return "Batch Face Swap" def show(self, is_img2img): return scripts.AlwaysVisible def ui(self, is_img2img): def updateVisualizer(searchSubdir: bool, howSplit: str, divider: int, maskWidth: int, maskHeight: int, input_path: str, visualizationOpacity: int, faceMode: int): facecfg = FaceDetectConfig(faceMode) # this is a huge pain to patch through so don't bother allFiles = [] totalNumberOfFaces = 0 usingFilenames = (input_path != '') if usingFilenames: allFiles = listFiles(input_path, searchSubdir, allFiles) if len(allFiles) > 0: file = allFiles[0] try: image = Image.open(file) maxsize = (1000, 500) image.thumbnail(maxsize,Image.ANTIALIAS) except (UnidentifiedImageError, AttributeError): allFiles = [] visualizationOpacity = (visualizationOpacity/100)*255 color = "white" thickness = 5 if "Both" in howSplit: onlyHorizontal = False onlyVertical = False if len(allFiles) == 0: image = Image.open("./extensions/batch-face-swap/images/exampleB.jpg") width, height = image.size # if len(masks)==0 and path != '': masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskWidth=maskWidth, maskHeight=maskHeight, skip=0) if len(masks) > 0: mask = masks[0] else: mask = np.zeros((image.height, image.width, 3), dtype=np.uint8) # mask = maskResize(mask, maskSize, height) mask = Image.fromarray(mask) redImage = Image.new("RGB", (width, height), (255, 0, 0)) mask = mask.convert("L") draw = ImageDraw.Draw(mask, "L") if divider > 1: for i in range(divider-1): start_point = (0, int((height/divider)*(i+1))) end_point = (int(width), int((height/divider)*(i+1))) draw.line([start_point, end_point], fill=color, width=thickness) for i in range(divider-1): start_point = (int((width/divider)*(i+1)), 0) end_point = (int((width/divider)*(i+1)), int(height)) draw.line([start_point, end_point], fill=color, width=thickness) image = composite(redImage, image, mask, visualizationOpacity) elif "Vertical" in howSplit: onlyHorizontal = False onlyVertical = True if len(allFiles) == 0: image = Image.open("./extensions/batch-face-swap/images/exampleV.jpg") # mask = Image.open("./extensions/batch-face-swap/images/exampleV_mask.jpg") # mask = np.array(mask) width, height = image.size # if len(masks)==0 and path != '': masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskWidth=maskWidth, maskHeight=maskHeight, skip=0) if len(masks) > 0: mask = masks[0] else: mask = np.zeros((image.height, image.width, 3), dtype=np.uint8) # mask = maskResize(mask, maskSize, height) mask = Image.fromarray(mask) redImage = Image.new("RGB", (width, height), (255, 0, 0)) mask = mask.convert("L") draw = ImageDraw.Draw(mask, "L") if divider > 1: for i in range(divider-1): start_point = (int((width/divider)*(i+1)), 0) end_point = (int((width/divider)*(i+1)), int(height)) draw.line([start_point, end_point], fill=color, width=thickness) image = composite(redImage, image, mask, visualizationOpacity) else: onlyHorizontal = True onlyVertical = False if len(allFiles) == 0: image = Image.open("./extensions/batch-face-swap/images/exampleH.jpg") width, height = image.size # if len(masks)==0 and path != '': masks, totalNumberOfFaces, faces_info, skip = findFaces(facecfg, image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskWidth=maskWidth, maskHeight=maskHeight, skip=0) if len(masks) > 0: mask = masks[0] else: mask = np.zeros((image.height, image.width, 3), dtype=np.uint8) # mask = maskResize(mask, maskSize, height) mask = Image.fromarray(mask) redImage = Image.new("RGB", (width, height), (255, 0, 0)) mask = mask.convert("L") draw = ImageDraw.Draw(mask, "L") if divider > 1: for i in range(divider-1): start_point = (0, int((height/divider)*(i+1))) end_point = (int(width), int((height/divider)*(i+1))) draw.line([start_point, end_point], fill=color, width=thickness) image = composite(redImage, image, mask, visualizationOpacity) update = gr.Image.update(value=image) return update def switchSaveMaskInteractivity(onlyMask: bool): return gr.Checkbox.update(interactive=bool(onlyMask)) def switchSaveMask(onlyMask: bool): if onlyMask == False: return gr.Checkbox.update(value=bool(onlyMask)) def switchTipsVisibility(showTips: bool): return gr.HTML.update(visible=bool(showTips)) def switchInvertMask(invertMask: bool): return gr.Checkbox.update(value=bool(invertMask)) def switchColumnVisibility(switch_element: bool): return gr.Column.update(visible=bool(switch_element)) def switchColumnVisibilityInverted(switch_element: bool): return gr.Column.update(visible=bool(not switch_element)) def switchEnableLabel(enabled: bool): if enabled == True: return gr.Checkbox.update(label=str("Enabled ✅")) else: return gr.Checkbox.update(label=str("Disabled ❌")) with gr.Accordion("🎭 Batch Face Swap 🎭", open = False, elem_id="batch_face_swap"): with gr.Row(): enabled = gr.Checkbox(label='Disabled ❌', value=False) if not is_img2img: with gr.Column(): regen_btn = gr.Button(value="Swap 🎭", variant="primary", interactive=True) gr.HTML("

Check your output folder (this won't show results in webui)

",visible=True) else: regen_btn = gr.Button(value="Swap 🎭", variant="primary", visible=False) with gr.Accordion("♻ Overrides ♻", open = True): with gr.Box(): # Overrides with gr.Column(): with gr.Column(variant='panel'): overridePrompt = gr.Checkbox(value=False, label="""Override "Prompt" """) with gr.Column(visible=False) as override_prompt_col: bfs_prompt = gr.Textbox(label="Prompt", show_label=False, lines=2, placeholder="Prompt") bfs_nprompt = gr.Textbox(label="Negative prompt", show_label=False, lines=2, placeholder="Negative prompt") with gr.Row(): with gr.Column(): with gr.Column(variant='panel', scale=2): overrideSeed = gr.Checkbox(value=True, label="""Override "Seed" to random""", interactive=True) with gr.Column(variant='panel'): overrideSampler = gr.Checkbox(value=False, label="""Override "Sampling method" """) with gr.Column(visible=False) as override_sampler_col: available_samplers = [s.name for s in modules.sd_samplers.samplers_for_img2img] sd_sampler = gr.Dropdown(label="Sampling Method", choices=available_samplers, value="Euler a", type="value", interactive=True) with gr.Column(variant='panel'): overrideSteps = gr.Checkbox(value=False, label="""Override "Sampling steps" """, interactive=True) with gr.Column(visible=False) as override_steps_col: steps = gr.Slider(minimum=1, maximum=150, step=1 , value=30, label="Sampling Steps", interactive=True) with gr.Column(variant='panel'): overrideDenoising = gr.Checkbox(value=True, label="""Override "Denoising strength" to 0.5""") with gr.Column(visible=False) as override_denoising_col: denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01 , value=0.5, label="Denoising Strength", interactive=True) with gr.Column(): with gr.Column(variant='panel', scale=2): overrideSize = gr.Checkbox(value=False, label="""Override "Resolution" """, interactive=True) with gr.Column(visible=False) as override_size_col: with gr.Row(): bfs_width = gr.Slider(minimum=64, maximum=2048, step=4 , value=512, label="Width", interactive=True) bfs_height = gr.Slider(minimum=64, maximum=2048, step=4 , value=512, label="Height", interactive=True) with gr.Column(variant='panel'): overrideModel = gr.Checkbox(value=False, label="""Override "Stable Diffusion checkpoint" """) with gr.Column(visible=False) as override_model_col: with gr.Row(): available_models = modules.sd_models.checkpoint_tiles() sd_model = gr.Dropdown(label="SD Model", choices=available_models, value=shared.sd_model.sd_checkpoint_info.title, type="value", interactive=True) modules.ui.create_refresh_button(sd_model, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_sd_checkpoint") with gr.Column(variant='panel'): overrideCfgScale = gr.Checkbox(value=False, label="""Override "CFG Scale" """, interactive=True) with gr.Column(visible=False) as override_cfg_col: cfg_scale = gr.Slider(minimum=1, maximum=30, step=1 , value=6, label="CFG Scale", interactive=True) with gr.Column(variant='panel'): overrideMaskBlur = gr.Checkbox(value=True, label="""Override "Mask blur" to automatic""") with gr.Column(visible=False) as override_maskBlur_col: mask_blur = gr.Slider(minimum=0, maximum=64, step=1 , value=4, label="Mask Blur", interactive=True) with gr.Column(variant='panel'): with gr.Row(): overridePadding = gr.Checkbox(value=True, label="""Override "Only masked padding, pixels" to automatic""") with gr.Row(): with gr.Column(visible=False) as override_padding_col: inpaint_full_res_padding = gr.Slider(minimum=0, maximum=256, step=4 , value=32, label="Only masked padding, pixels", interactive=True) if is_img2img: # Path to images gr.HTML("

Input:

") with gr.Column(variant='panel'): htmlTip1 = gr.HTML("

'Load from subdirectories' will include all images in all subdirectories.

",visible=False) with gr.Row(): input_path = gr.Textbox(label="Images directory",placeholder=r"C:\Users\dude\Desktop\images", visible=True) output_path = gr.Textbox(label="Output directory (OPTIONAL)",placeholder=r"Leave empty to save to default directory") with gr.Row(): searchSubdir = gr.Checkbox(value=False, label="Load from subdirectories") saveToOriginalFolder = gr.Checkbox(value=False, label="Save to original folder") keepOriginalName = gr.Checkbox(value=False, label="Keep original file name (OVERWRITES FILES WITH THE SAME NAME)") loadGenParams = gr.Checkbox(value=False, label="Load generation parameters from images") else: htmlTip1 = gr.HTML("

",visible=False) input_path = gr.Textbox(label="Images directory", visible=False) output_path = gr.Textbox(label="Output directory (OPTIONAL)", visible=False) searchSubdir = gr.Checkbox(value=False, label="Load from subdirectories", visible=False) saveToOriginalFolder = gr.Checkbox(value=False, label="Save to original folder", visible=False) keepOriginalName = gr.Checkbox(value=False, label="Keep original file name (OVERWRITES FILES WITH THE SAME NAME)", visible=False) loadGenParams = gr.Checkbox(value=False, label="Load generation parameters from images", visible=False) with gr.Accordion("⚙️ Settings ⚙️", open = False): with gr.Column(variant='panel'): with gr.Tab("Generate masks") as generateMasksTab: # Face detection with gr.Column(variant='compact'): gr.HTML("

Face detection:

") with gr.Row(): faceDetectMode = gr.Dropdown(label="Detector", choices=face_mode_names, value=face_mode_names[FaceMode.DEFAULT], type="index", elem_id="z_type") minFace = gr.Slider(minimum=10, maximum=200, step=1 , value=30, label="Minimum face size in pixels") with gr.Column(variant='panel'): htmlTip2 = gr.HTML("

Activate the 'Masks only' checkbox to see how many faces do your current settings detect without generating SD image. (check console)

You can also save generated masks to disk. Only possible with 'Masks only' (if you leave path empty, it will save the masks to your default webui outputs directory)

'Single mask per image' is only recommended with 'Invert mask' or if you want to save one mask per image, not per face. If you activate it without inverting mask, and try to process an image with multiple faces, it will generate only one image for all faces, producing bad results.

'Rotation threshold', if the face is rotated at an angle higher than this value, it will be automatically rotated so it's upright before generating, producing much better results.

",visible=False) # Settings with gr.Column(variant='panel'): gr.HTML("

Settings:

") with gr.Column(variant='compact'): with gr.Row(): onlyMask = gr.Checkbox(value=False, label="Masks only", visible=True) saveMask = gr.Checkbox(value=False, label="Save masks to disk", interactive=False) with gr.Row(): invertMask = gr.Checkbox(value=False, label="Invert mask", visible=True) singleMaskPerImage = gr.Checkbox(value=False, label="Single mask per image", visible=True) with gr.Row(variant='panel'): rotation_threshold = gr.Slider(minimum=0, maximum=180, step=1, value=20, label="Rotation threshold") # Image splitter with gr.Column(variant='panel'): gr.HTML("

Image splitter:

") with gr.Column(variant='panel'): htmlTip3 = gr.HTML("

This divides image to smaller images and tries to find a face in the individual smaller images.

Useful when faces are small in relation to the size of the whole picture and are not being detected.

(may result in mask that only covers a part of a face or no detection if the division goes right through the face)

Open 'Split visualizer' to see how it works.

",visible=False) with gr.Row(): divider = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="How many images to divide into") maskWidth = gr.Slider(minimum=0, maximum=300, step=1, value=100, label="Mask width") with gr.Row(): howSplit = gr.Radio(["Horizontal only ▤", "Vertical only ▥", "Both ▦"], value = "Both ▦", label = "How to divide") maskHeight = gr.Slider(minimum=0, maximum=300, step=1, value=100, label="Mask height") with gr.Accordion(label="Visualizer", open=False): exampleImage = gr.Image(value=Image.open("./extensions/batch-face-swap/images/exampleB.jpg"), label="Split visualizer", show_label=False, type="pil", visible=True).style(height=500) with gr.Row(variant='compact'): with gr.Column(variant='panel'): gr.HTML("", visible=False) with gr.Column(variant='compact'): visualizationOpacity = gr.Slider(minimum=0, maximum=100, step=1, value=75, label="Opacity") # Other with gr.Column(variant='panel'): gr.HTML("

Other:

") with gr.Column(variant='panel'): htmlTip4 = gr.HTML("

'Count faces before generating' is required to see accurate progress bar (not recommended when processing a large number of images). Because without knowing the number of faces, the webui can't know how many images it will generate. Activating it means you will search for faces twice.

",visible=False) saveNoFace = gr.Checkbox(value=True, label="Save image even if face was not found") countFaces = gr.Checkbox(value=False, label="Count faces before generating (accurate progress bar but NOT recommended)") with gr.Tab("Existing masks",) as existingMasksTab: with gr.Column(variant='panel'): htmlTip5 = gr.HTML("

Image name and it's corresponding mask must have exactly the same name (if image is called `abc.jpg` then it's mask must also be called `abc.jpg`)

",visible=False) pathExisting = gr.Textbox(label="Images directory",placeholder=r"C:\Users\dude\Desktop\images") pathMasksExisting = gr.Textbox(label="Masks directory",placeholder=r"C:\Users\dude\Desktop\masks") output_pathExisting = gr.Textbox(label="Output directory (OPTIONAL)",placeholder=r"Leave empty to save to default directory") # General with gr.Box(): with gr.Column(variant='panel'): gr.HTML("

General:

") htmlTip6 = gr.HTML("

Activate 'Show results in WebUI' checkbox to see results in the WebUI at the end (not recommended when processing a large number of images)

",visible=False) with gr.Row(): viewResults = gr.Checkbox(value=True, label="Show results in WebUI") showTips = gr.Checkbox(value=False, label="Show tips") # Face detect internals with gr.Column(variant='panel', visible = FaceDetectDevelopment): gr.HTML("

Debug internal config:

") with gr.Column(variant='panel'): with gr.Row(): debugSave = gr.Checkbox(value=False, label="Save debug images") optimizeDetect= gr.Checkbox(value=True, label="Used optimized detector") face_x_scale = gr.Slider(minimum=1 , maximum= 6, step=0.1, value=4, label="Face x-scaleX") face_y_scale = gr.Slider(minimum=1 , maximum= 6, step=0.1, value=2.5, label="Face y-scaleX") multiScale = gr.Slider(minimum=1.0, maximum=200, step=0.001, value=1.03, label="Multiscale search stepsizess") multiScale2 = gr.Slider(minimum=0.8, maximum=200, step=0.001, value=1.0 , label="Multiscale search secondary scalar") multiScale3 = gr.Slider(minimum=0.8, maximum=2.0, step=0.001, value=1.0 , label="Multiscale search tertiary scale") minNeighbors = gr.Slider(minimum=1 , maximum = 10, step=1 , value=5, label="minNeighbors") mpconfidence = gr.Slider(minimum=0.01, maximum = 2.0, step=0.01, value=0.5, label="FaceMesh confidence threshold") mpcount = gr.Slider(minimum=1, maximum = 20, step=1, value=5, label="FaceMesh maximum faces") # def retriveP(getp: bool): # getp = gr.Checkbox(value=True, label="get p", visible=False) mainTab = gr.Textbox(value=f"""{"img2img" if is_img2img else "txt2img"}""", visible=False) selectedTab = gr.Textbox(value="generateMasksTab", visible=False) generateMasksTab.select(lambda: "generateMasksTab", inputs=None, outputs=selectedTab) existingMasksTab.select(lambda: "existingMasksTab", inputs=None, outputs=selectedTab) # make sure user is in the "Inpaint upload" tab input_path.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) output_path.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) searchSubdir.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) saveToOriginalFolder.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) keepOriginalName.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) loadGenParams.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) def regen(input_path: str, searchSubdir: bool, viewResults: bool, divider: int, howSplit: str, saveMask: bool, output_path: str, saveToOriginalFolder: bool, onlyMask: bool, saveNoFace: bool, overridePrompt: bool, bfs_prompt: str, bfs_nprompt: str, overrideSampler: bool, sd_sampler: str, overrideModel: bool, sd_model: str, overrideDenoising: bool, denoising_strength: float, overrideMaskBlur: bool, mask_blur: float, overridePadding: bool, inpaint_full_res_padding: float, overrideSeed: bool, overrideSteps: bool, steps: float, overrideCfgScale: bool, cfg_scale: float, overrideSize: bool, bfs_width: float, bfs_height: float, invertMask: bool, singleMaskPerImage: bool, countFaces: bool, maskWidth: float, maskHeight: float, keepOriginalName: bool, pathExisting: str, pathMasksExisting: str, output_pathExisting: str, selectedTab: str, mainTab: str, loadGenParams: bool, rotation_threshold: float, faceDetectMode: str, face_x_scale: float, face_y_scale: float, minFace: float, multiScale: float, multiScale2: float, multiScale3: float, minNeighbors: float, mpconfidence: float, mpcount: float, debugSave: bool, optimizeDetect: bool): try: p=original_p image = input_image except NameError: print("Make sure you generated an image first!") return facecfg = FaceDetectConfig(faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect) finishedImages = generateImages(p, facecfg, image, input_path, searchSubdir, viewResults, int(divider), howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, mainTab, loadGenParams, rotation_threshold) regen_btn.click(fn=regen, inputs=[input_path, searchSubdir, viewResults, divider, howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, mainTab, loadGenParams, rotation_threshold, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect], outputs=None) enabled.change(switchEnableLabel, enabled, enabled) onlyMask.change(switchSaveMaskInteractivity, onlyMask, saveMask) onlyMask.change(switchSaveMask, onlyMask, saveMask) invertMask.change(switchInvertMask, invertMask, singleMaskPerImage) faceDetectMode.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) minFace.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) visualizationOpacity.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) searchSubdir.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) howSplit.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) divider.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) maskWidth.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) maskHeight.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) input_path.change(updateVisualizer, [searchSubdir, howSplit, divider, maskWidth, maskHeight, input_path, visualizationOpacity, faceDetectMode], exampleImage) overridePrompt.change(switchColumnVisibility, overridePrompt, override_prompt_col) overrideSize.change(switchColumnVisibility, overrideSize, override_size_col) overrideSteps.change(switchColumnVisibility, overrideSteps, override_steps_col) overrideCfgScale.change(switchColumnVisibility, overrideCfgScale, override_cfg_col) overrideSampler.change(switchColumnVisibility, overrideSampler, override_sampler_col) overrideModel.change(switchColumnVisibility, overrideModel, override_model_col) overrideDenoising.change(switchColumnVisibilityInverted, overrideDenoising, override_denoising_col) overrideMaskBlur.change(switchColumnVisibilityInverted, overrideMaskBlur, override_maskBlur_col) overridePadding.change(switchColumnVisibilityInverted, overridePadding, override_padding_col) showTips.change(switchTipsVisibility, showTips, htmlTip1) showTips.change(switchTipsVisibility, showTips, htmlTip2) showTips.change(switchTipsVisibility, showTips, htmlTip3) showTips.change(switchTipsVisibility, showTips, htmlTip4) showTips.change(switchTipsVisibility, showTips, htmlTip5) showTips.change(switchTipsVisibility, showTips, htmlTip6) return [enabled, mainTab, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, input_path, searchSubdir, divider, howSplit, saveMask, output_path, saveToOriginalFolder, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect, loadGenParams, rotation_threshold] def process(self, p, enabled, mainTab, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, input_path, searchSubdir, divider, howSplit, saveMask, output_path, saveToOriginalFolder, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect, loadGenParams, rotation_threshold): global original_p global all_images original_p = p if enabled and mainTab == "img2img": wasGrid = p.do_not_save_grid p.do_not_save_grid = True all_images = [] facecfg = FaceDetectConfig(faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect) if input_path == '': input_image = [ p.init_images[0] ] else: input_image = None finishedImages = generateImages(p, facecfg, input_image, input_path, searchSubdir, viewResults, int(divider), howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, mainTab, loadGenParams, rotation_threshold) if not viewResults: finishedImages = [] all_images += finishedImages proc = Processed(p, all_images) # doing this to prevent starting another img2img generation p.batch_size = 1 p.n_iter = 0 p.init_images[0] = all_images[0] p.do_not_save_grid = wasGrid return proc else: pass def postprocess(self, p, processed, enabled, mainTab, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, input_path, searchSubdir, divider, howSplit, saveMask, output_path, saveToOriginalFolder, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect, loadGenParams, rotation_threshold): global all_images global input_image if input_path == '': input_image = [] input_image += processed.images if enabled and mainTab == "txt2img": wasGrid = p.do_not_save_grid p.do_not_save_grid = True all_images = [] facecfg = FaceDetectConfig(faceDetectMode, face_x_scale, face_y_scale, minFace, multiScale, multiScale2, multiScale3, minNeighbors, mpconfidence, mpcount, debugSave, optimizeDetect) finishedImages = generateImages(p, facecfg, input_image, input_path, searchSubdir, viewResults, int(divider), howSplit, saveMask, output_path, saveToOriginalFolder, onlyMask, saveNoFace, overridePrompt, bfs_prompt, bfs_nprompt, overrideSampler, sd_sampler, overrideModel, sd_model, overrideDenoising, denoising_strength, overrideMaskBlur, mask_blur, overridePadding, inpaint_full_res_padding, overrideSeed, overrideSteps, steps, overrideCfgScale, cfg_scale, overrideSize, bfs_width, bfs_height, invertMask, singleMaskPerImage, countFaces, maskWidth, maskHeight, keepOriginalName, pathExisting, pathMasksExisting, output_pathExisting, selectedTab, mainTab, loadGenParams, rotation_threshold) if not viewResults: finishedImages = [] all_images += finishedImages p.do_not_save_grid = wasGrid processed.images = all_images elif enabled and mainTab == "img2img": processed.images = all_images else: pass # def on_ui_settings(): # section = ('bfs', "BatchFaceSwap") # shared.opts.add_option("bfs_override_prompt", shared.OptionInfo( # False, "Default state of Override Prompt", gr.Checkbox, {"interactive": True}, section=section)) # shared.opts.add_option("bfs_prompt", shared.OptionInfo( # "", "Default Prompt", section=section)) # shared.opts.add_option("bfs_nprompt", shared.OptionInfo( # "", "Default Negative Prompt", section=section)) # script_callbacks.on_ui_settings(on_ui_settings)