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 * import modules.scripts as scripts import gradio as gr from modules import images, masking from modules.processing import process_images, create_infotext, Processed from modules.shared import opts, cmd_opts, state import cv2 import numpy as np from PIL import Image, ImageOps, ImageDraw, ImageFilter, UnidentifiedImageError import math def findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, skip): masks = [] imageOriginal = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) heightOriginal = height widthOriginal = width # Calculate the size of each small image if onlyVertical == True: small_width = math.ceil(width / divider) small_height = height elif onlyHorizontal == True: small_width = width small_height = math.ceil(height / divider) else: small_width = math.ceil(width / divider) small_height = 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 = cv2.cvtColor(np.array(small_image), cv2.COLOR_RGB2BGR) landmarks = [] landmarks = getFacialLandmarks(small_image) numberOfFaces = int(len(landmarks)) totalNumberOfFaces += numberOfFaces if countFaces: return totalNumberOfFaces faces = [] for landmark in landmarks: convexhull = cv2.convexHull(landmark) faces.append(convexhull) if len(landmarks) == 0: small_image[:] = (0, 0, 0) if numberOfFaces > 0: facesInImage += numberOfFaces if facesInImage == 0 and i == len(small_images) - 1: skip = 1 mask = np.zeros((small_height, small_width), np.uint8) for i in range(len(landmarks)): small_image = cv2.fillConvexPoly(mask, faces[i], 255) processed_image = Image.fromarray(small_image) processed_images.append(processed_image) if file != None: print(f"Found {facesInImage} face(s) in {str(file)}") # else: # print(f"Found {facesInImage} face(s)") # 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)) image = cv2.cvtColor(np.array(new_image), cv2.COLOR_RGB2BGR) imageOriginal[:] = (0, 0, 0) imageOriginal[0:heightOriginal, 0:widthOriginal] = image[0:height, 0:width] # convert to grayscale imageOriginal = cv2.cvtColor(imageOriginal, cv2.COLOR_RGB2GRAY) # convert grayscale to binary thresh = 100 imageOriginal = cv2.threshold(imageOriginal,thresh,255,cv2.THRESH_BINARY)[1] binary_image = cv2.convertScaleAbs(imageOriginal) try: kernel = np.ones((int(math.ceil(0.011*height)),int(math.ceil(0.011*height))),'uint8') dilated = cv2.dilate(binary_image,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 maskSize != 0: mask = maskResize(mask, maskSize, height) if not singleMaskPerImage: if facesInImage > 1: segmentFaces = True while (segmentFaces): currentBiggest = findBiggestFace(mask) masks.append(currentBiggest) mask = mask - currentBiggest whitePixels = cv2.countNonZero(mask) whitePixelThreshold = 0.0005 * (widthOriginal * heightOriginal) if (whitePixels < whitePixelThreshold): segmentFaces = False return masks, totalNumberOfFaces, skip masks.append(mask) return masks, totalNumberOfFaces, skip def faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, pathToSave, info, selectedTab): p.do_not_save_samples = True if len(masks) == 1: if selectedTab == "existingMasksTab": mask = masks[0] else: mask = Image.fromarray(masks[0]) if invertMask: mask = ImageOps.invert(mask) p.init_images = [image] p.image_mask = mask proc = process_images(p) if pathToSave != "": for n in range(p.batch_size): if opts.samples_format == "png": images.save_image(proc.images[n], pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(n+1) if forced_filename != None and p.batch_size > 1 else forced_filename) elif image.mode != 'RGB': image = image.convert('RGB') images.save_image(proc.images[n], pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(n+1) if forced_filename != None and p.batch_size > 1 else forced_filename) else: images.save_image(proc.images[n], pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(n+1) if forced_filename != None and p.batch_size > 1 else forced_filename) else: for n in range(p.batch_size): if opts.samples_format == "png": images.save_image(proc.images[n], p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(n+1) if forced_filename != None and p.batch_size > 1 else forced_filename) elif image.mode != 'RGB': image = image.convert('RGB') images.save_image(proc.images[n], p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(n+1) if forced_filename != None and p.batch_size > 1 else forced_filename) else: images.save_image(proc.images[n], p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(n+1) if forced_filename != None and p.batch_size > 1 else forced_filename) finishedImages.append(proc.images[n]) else: 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') crop_region = masking.get_crop_region(np.array(mask), p.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)) 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) p.init_images = [image] p.image_mask = image_mask proc = process_images(p) generatedImages.append(proc.images) image = imageOriginal for j in range(p.batch_size): image = imageOriginal for k in range(len(generatedImages)): mask = Image.fromarray(masks[k]) mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur)) image = apply_overlay(generatedImages[k][j], paste_to[k], image, mask) if pathToSave != "": if opts.samples_format == "png": images.save_image(image, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(j+1) if forced_filename != None and p.batch_size > 1 else forced_filename) elif image.mode != 'RGB': image = image.convert('RGB') images.save_image(image, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(j+1) if forced_filename != None and p.batch_size > 1 else forced_filename) else: images.save_image(image, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(j+1) if forced_filename != None and p.batch_size > 1 else forced_filename) else: if opts.samples_format == "png": images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(j+1) if forced_filename != None and p.batch_size > 1 else forced_filename) elif image.mode != 'RGB': image = image.convert('RGB') images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(j+1) if forced_filename != None and p.batch_size > 1 else forced_filename) else: images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename+"_"+str(j+1) if forced_filename != None and p.batch_size > 1 else forced_filename) finishedImages.append(image) p.do_not_save_samples = False return finishedImages def generateImages(p, path, searchSubdir, viewResults, divider, howSplit, saveMask, pathToSave, onlyMask, saveNoFace, overrideDenoising, overrideMaskBlur, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, info, pathExisting, pathMasksExisting, pathToSaveExisting, selectedTab): if selectedTab == "generateMasksTab": wasCountFaces = False finishedImages = [] totalNumberOfFaces = 0 allFiles = [] if howSplit == "Horizontal only ▤": onlyHorizontal = True onlyVertical = False elif howSplit == "Vertical only ▥": onlyHorizontal = False onlyVertical = True elif howSplit == "Both ▦": onlyHorizontal = False onlyVertical = False # RUN IF PATH IS INSERTED if path != '': allFiles = listFiles(path, searchSubdir, allFiles) if countFaces: print("\nCounting faces...") for i, file in enumerate(allFiles): skip = 0 image = Image.open(file) width, height = image.size totalNumberOfFaces = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, 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 keepOriginalName: forced_filename = os.path.splitext(os.path.basename(file))[0] else: forced_filename = None 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) width, height = image.size except UnidentifiedImageError: print(f"\nUnable to open {file}, skipping") continue if not onlyMask: if overrideDenoising == True: p.denoising_strength = 0.5 if overrideMaskBlur == True: p.mask_blur = int(math.ceil(0.01*height)) skip = 0 masks, totalNumberOfFaces, skip = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, file, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, skip) # Only generate mask if onlyMask: suffix = '_mask' # If path to save mask was provided if pathToSave != "": for i, mask in enumerate(masks): mask = Image.fromarray(mask) # Invert mask if needed if invertMask: mask = ImageOps.invert(mask) finishedImages.append(mask) # Save mask if saveMask == True: if opts.samples_format == "png": images.save_image(mask, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) elif mask.mode != 'RGB': mask = mask.convert('RGB') images.save_image(mask, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) else: images.save_image(mask, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) # If path to save mask was NOT provided elif pathToSave == "": for i, mask in enumerate(masks): mask = Image.fromarray(mask) # Invert mask if needed if invertMask: mask = ImageOps.invert(mask) finishedImages.append(mask) # Save mask if saveMask == True: if opts.samples_format == "png": images.save_image(mask, opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) elif mask.mode != 'RGB': mask = mask.convert('RGB') images.save_image(mask, opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) else: images.save_image(mask, opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) # If face was not found but user wants to save images without face if skip == 1 and saveNoFace and not onlyMask: if opts.samples_format == "png": images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename) elif image.mode != 'RGB': image = image.convert('RGB') images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename) else: images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename) 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, pathToSave, info, selectedTab) if not viewResults: finishedImages = [] if wasCountFaces == True: countFaces = True print(f"Found {totalNumberOfFaces} faces in {len(allFiles)} images.") # RUN IF PATH IS NOT INSERTED AND IMAGE IS if path == '' and p.init_images[0] != None: forced_filename = None image = p.init_images[0] width, height = image.size if countFaces: print("\nCounting faces...") skip = 0 totalNumberOfFaces = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, None, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, skip) if not onlyMask and countFaces: print(f"\nWill process {len(p.init_images)} 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(p.init_images)} images, creating {p.n_iter * p.batch_size} new images for each.") state.job_count = len(p.init_images) * p.n_iter if countFaces: state.job = f"{1} out of {totalNumberOfFaces}" totalNumberOfFaces = 0 wasCountFaces = True countFaces = False else: state.job = f"{1} out of {len(p.init_images)}" if not onlyMask: if overrideDenoising == True: p.denoising_strength = 0.5 if overrideMaskBlur == True: p.mask_blur = int(math.ceil(0.01*height)) skip = 0 masks, totalNumberOfFaces, skip = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, None, totalNumberOfFaces, singleMaskPerImage, countFaces, maskSize, skip) # Only generate mask if onlyMask: suffix = '_mask' # If path to save mask was provided if pathToSave != "": for i, mask in enumerate(masks): mask = Image.fromarray(mask) # Invert mask if needed if invertMask: mask = ImageOps.invert(mask) finishedImages.append(mask) # Save mask if saveMask == True: if opts.samples_format == "png": images.save_image(mask, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) elif mask.mode != 'RGB': mask = mask.convert('RGB') images.save_image(mask, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) else: images.save_image(mask, pathToSave, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) # If path to save mask was NOT provided elif pathToSave == "": for i, mask in enumerate(masks): mask = Image.fromarray(mask) # Invert mask if needed if invertMask: mask = ImageOps.invert(mask) finishedImages.append(mask) # Save mask if saveMask == True: if opts.samples_format == "png": images.save_image(mask, opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) elif mask.mode != 'RGB': mask = mask.convert('RGB') images.save_image(mask, opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) else: images.save_image(mask, opts.outdir_img2img_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename, suffix=suffix) # If face was not found but user wants to save images without face if skip == 1 and saveNoFace and not onlyMask: if opts.samples_format == "png": images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename) elif image.mode != 'RGB': image = image.convert('RGB') images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename) else: images.save_image(image, p.outpath_samples, "", p.seed, p.prompt, opts.samples_format, info=info, p=p, forced_filename=forced_filename) finishedImages.append(image) state.skipped = True # If face was not found, just skip if skip == 1: state.skipped = True if not onlyMask: finishedImages = faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, pathToSave, info, selectedTab) if wasCountFaces == True: countFaces = True print(f"Found {totalNumberOfFaces} faces in {len(p.init_images)} images.") elif selectedTab == "existingMasksTab": finishedImages = [] allImages = [] searchSubdir = False if pathExisting != '' and pathMasksExisting != '': allImages = listFiles(pathExisting, searchSubdir, allImages) 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.basename(file)))) except UnidentifiedImageError: print(f"\nUnable to open {file}, skipping") continue if overrideDenoising == True: p.denoising_strength = 0.5 if overrideMaskBlur == True: p.mask_blur = int(math.ceil(0.01*height)) finishedImages = faceSwap(p, masks, image, finishedImages, invertMask, forced_filename, pathToSaveExisting, info, selectedTab) if not viewResults: finishedImages = [] return finishedImages class Script(scripts.Script): def title(self): return "Batch Face Swap" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): def updateVisualizer(searchSubdir: bool, howSplit: str, divider: int, maskSize: int, path: str, visualizationOpacity: int): allFiles = [] totalNumberOfFaces = 0 if path != '': allFiles = listFiles(path, searchSubdir, allFiles) if len(allFiles) > 0: imgPath = allFiles[0] try: image = Image.open(imgPath) maxsize = (1000, 500) image.thumbnail(maxsize,Image.ANTIALIAS) except UnidentifiedImageError: 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") # mask = Image.open("./extensions/batch-face-swap/images/exampleB_mask.jpg") # mask = np.array(mask) width, height = image.size # if len(masks)==0 and path != '': masks, totalNumberOfFaces, skip = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskSize=maskSize, skip=0) mask = masks[0] mask = maskResize(mask, maskSize, height) mask = Image.fromarray(mask) redImage = Image.new("RGB", (width, height), (255, 0, 0)) 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, skip = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskSize=maskSize, skip=0) mask = masks[0] mask = maskResize(mask, maskSize, height) mask = Image.fromarray(mask) redImage = Image.new("RGB", (width, height), (255, 0, 0)) 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") # mask = Image.open("./extensions/batch-face-swap/images/exampleH_mask.jpg") # mask = np.array(mask) width, height = image.size # if len(masks)==0 and path != '': masks, totalNumberOfFaces, skip = findFaces(image, width, height, divider, onlyHorizontal, onlyVertical, file=None, totalNumberOfFaces=totalNumberOfFaces, singleMaskPerImage=True, countFaces=False, maskSize=maskSize, skip=0) mask = masks[0] mask = maskResize(mask, maskSize, height) mask = Image.fromarray(mask) redImage = Image.new("RGB", (width, height), (255, 0, 0)) 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)) with gr.Column(variant='panel'): gr.HTML("

Make sure you're in the \"Inpaint upload\" tab!

") with gr.Box(): # Overrides with gr.Column(variant='panel'): gr.HTML("

Overrides:

") with gr.Row(): overrideDenoising = gr.Checkbox(value=True, label="""Override "Denoising strength" to 0.5""") with gr.Row(): overrideMaskBlur = gr.Checkbox(value=True, label="""Override "Mask blur" to automatic""") with gr.Column(variant='panel'): with gr.Tab("Generate masks") as generateMasksTab: with gr.Column(variant='panel'): htmlTip1 = 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.

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

Settings:

") with gr.Column(): 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) # Path to images with gr.Column(variant='panel'): gr.HTML("

Path to images:

") with gr.Column(variant='panel'): htmlTip2 = gr.HTML("

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

",visible=False) with gr.Row(): path = gr.Textbox(label="Images directory",placeholder=r"C:\Users\dude\Desktop\images") pathToSave = gr.Textbox(label="Output directory (OPTIONAL)",placeholder=r"Leave empty to save to default directory") searchSubdir = gr.Checkbox(value=False, label="Load from subdirectories") keepOriginalName = gr.Checkbox(value=False, label="Keep original file name (OVERWRITES FILES WITH THE SAME NAME)") # 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") maskSize = gr.Slider(minimum=-10, maximum=10, step=1, value=0, label="Mask size") howSplit = gr.Radio(["Horizontal only ▤", "Vertical only ▥", "Both ▦"], value = "Both ▦", label = "How to divide") 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") pathToSaveExisting = 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:

") with gr.Column(variant='panel'): 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=False, label="Show results in WebUI") showTips = gr.Checkbox(value=False, label="Show tips") selectedTab = gr.Textbox(value="generateMasksTab", visible=False) generateMasksTab.select(lambda: "generateMasksTab", inputs=None, outputs=selectedTab) existingMasksTab.select(lambda: "existingMasksTab", inputs=None, outputs=selectedTab) pathExisting.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) pathMasksExisting.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) pathToSaveExisting.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) path.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) pathToSave.change(fn=None, _js="gradioApp().getElementById('mode_img2img').querySelectorAll('button')[4].click()", inputs=None, outputs=None) onlyMask.change(switchSaveMaskInteractivity, onlyMask, saveMask) onlyMask.change(switchSaveMask, onlyMask, saveMask) invertMask.change(switchInvertMask, invertMask, singleMaskPerImage) visualizationOpacity.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, path, visualizationOpacity], exampleImage) searchSubdir.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, path, visualizationOpacity], exampleImage) howSplit.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, path, visualizationOpacity], exampleImage) divider.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, path, visualizationOpacity], exampleImage) maskSize.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, path, visualizationOpacity], exampleImage) path.change(updateVisualizer, [searchSubdir, howSplit, divider, maskSize, path, visualizationOpacity], exampleImage) 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 [overrideDenoising, overrideMaskBlur, path, searchSubdir, divider, howSplit, saveMask, pathToSave, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, pathToSaveExisting, selectedTab] def run(self, p, overrideDenoising, overrideMaskBlur, path, searchSubdir, divider, howSplit, saveMask, pathToSave, viewResults, saveNoFace, onlyMask, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, pathExisting, pathMasksExisting, pathToSaveExisting, selectedTab): comments = {} def infotext(iteration=0, position_in_batch=0): if p.all_prompts == None: p.all_prompts = [p.prompt] if p.all_negative_prompts == None: p.all_negative_prompts = [p.negative_prompt] if p.all_seeds == None: p.all_seeds = [p.seed] if p.all_subseeds == None: p.all_subseeds = [p.subseed] return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch) info = infotext() all_images = [] finishedImages = generateImages(p, path, searchSubdir, viewResults, int(divider), howSplit, saveMask, pathToSave, onlyMask, saveNoFace, overrideDenoising, overrideMaskBlur, invertMask, singleMaskPerImage, countFaces, maskSize, keepOriginalName, info, pathExisting, pathMasksExisting, pathToSaveExisting, selectedTab) if not viewResults: finishedImages = [] all_images += finishedImages proc = Processed(p, all_images) return proc