new file: haar_cascade_models/animeface.xml
new file: haar_cascade_models/hand_gesture.xml modified: mikey_nodes.py new FaceFixerOpenCV
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@@ -3095,6 +3095,229 @@ class MikeySamplerTiledBaseOnly(MikeySamplerTiled):
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#final_image = pil2tensor(tiled_image)
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return (tiled_image,)
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"""
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import cv2
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# Load a pre-trained face detection model
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Read the image where you want to detect faces
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image_path = 'path_to_your_image.jpg' # Replace with your image path
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image = cv2.imread(image_path)
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# Convert the image to grayscale (needed for face detection)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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# Draw rectangles around each face
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for (x, y, w, h) in faces:
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cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
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# Display the output
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cv2.imshow('Face Detection', image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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"""
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class FaceFixerOpenCV:
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@classmethod
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def INPUT_TYPES(s):
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classifiers = ['animeface','combined','haarcascade_frontalface_default.xml', 'haarcascade_profileface.xml',
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'haarcascade_frontalface_alt.xml', 'haarcascade_frontalface_alt2.xml',
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'haarcascade_upperbody.xml', 'haarcascade_fullbody.xml', 'haarcascade_lowerbody.xml',
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'haarcascade_frontalcatface.xml', 'hands']
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return {"required": {"image": ("IMAGE",), "base_model": ("MODEL",), "vae": ("VAE",),
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"positive_cond_base": ("CONDITIONING",), "negative_cond_base": ("CONDITIONING",),
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#"model_name": (folder_paths.get_filename_list("upscale_models"), ), USING LANCZOS INSTEAD OF MODEL
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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#"upscale_by": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 10.0, "step": 0.1}),
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"face_img_resolution": ("INT", {"default": 1024, "min": 512, "max": 2048}),
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"padding": ("INT", {"default": 32, "min": 0, "max": 512}),
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"scale_factor": ("FLOAT", {"default": 1.2, "min": 0.1, "max": 10.0, "step": 0.1}),
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"min_neighbors": ("INT", {"default": 8, "min": 1, "max": 100}),
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"denoise": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"classifier": (classifiers, {"default": 'combined'}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {'default': 'dpmpp_2m_sde'}),
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"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {'default': 'karras'}),
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"cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
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"steps": ("INT", {"default": 30, "min": 1, "max": 1000})}}
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RETURN_TYPES = ('IMAGE',)
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RETURN_NAMES = ('image',)
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FUNCTION = 'run'
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CATEGORY = 'Mikey/Utils'
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def calculate_iou(self, box1, box2):
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"""
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Calculate the Intersection over Union (IoU) of two bounding boxes.
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Parameters:
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box1, box2: The bounding boxes, each defined as [x, y, width, height]
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Returns:
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iou: Intersection over Union as a float.
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"""
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# Determine the coordinates of each of the boxes
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x1_min, y1_min, x1_max, y1_max = box1[0], box1[1], box1[0] + box1[2], box1[1] + box1[3]
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x2_min, y2_min, x2_max, y2_max = box2[0], box2[1], box2[0] + box2[2], box2[1] + box2[3]
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# Calculate the intersection area
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intersect_x_min = max(x1_min, x2_min)
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intersect_y_min = max(y1_min, y2_min)
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intersect_x_max = min(x1_max, x2_max)
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intersect_y_max = min(y1_max, y2_max)
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intersect_area = max(0, intersect_x_max - intersect_x_min) * max(0, intersect_y_max - intersect_y_min)
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# Calculate the union area
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box1_area = (x1_max - x1_min) * (y1_max - y1_min)
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box2_area = (x2_max - x2_min) * (y2_max - y2_min)
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union_area = box1_area + box2_area - intersect_area
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# Calculate the IoU
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iou = intersect_area / union_area if union_area != 0 else 0
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return iou
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def detect_faces(self, image, classifier, scale_factor, min_neighbors):
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# before running check if cv2 is installed
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try:
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import cv2
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except ImportError:
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raise Exception('OpenCV is not installed. Please install it using "pip install opencv-python"')
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# detect face
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if classifier == 'animeface':
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p = os.path.dirname(os.path.realpath(__file__))
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p = os.path.join(p, 'haar_cascade_models/animeface.xml')
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elif classifier == 'hands':
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p = os.path.dirname(os.path.realpath(__file__))
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p = os.path.join(p, 'haar_cascade_models/hand_gesture.xml')
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else:
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p = cv2.data.haarcascades + classifier
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face_cascade = cv2.CascadeClassifier(p)
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# convert to numpy array
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image_np = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
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# Convert the image to grayscale (needed for face detection)
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gray = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = face_cascade.detectMultiScale(gray, scaleFactor=scale_factor, minNeighbors=min_neighbors, minSize=(32, 32))
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return faces
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def combo_detection(self, image, scale_factor, min_neighbors):
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# front faces
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front_faces = self.detect_faces(image, 'haarcascade_frontalface_default.xml', scale_factor, min_neighbors)
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# profile faces
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profile_faces = self.detect_faces(image, 'haarcascade_profileface.xml', scale_factor, min_neighbors)
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# anime faces
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anime_faces = self.detect_faces(image, 'animeface', scale_factor, min_neighbors)
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# if no faces detected
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if front_faces == () and profile_faces == () and anime_faces == ():
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return front_faces
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if front_faces == () and profile_faces != () and anime_faces == ():
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return profile_faces
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if front_faces != () and profile_faces == () and anime_faces == ():
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return front_faces
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if front_faces == () and profile_faces == () and anime_faces != ():
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return anime_faces
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# combined faces
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arrays = []
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if front_faces != ():
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arrays.append(front_faces)
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if profile_faces != ():
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arrays.append(profile_faces)
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if anime_faces != ():
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arrays.append(anime_faces)
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combined_faces = np.concatenate(arrays, axis=0)
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# removing duplicates
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iou_threshold = 0.2
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faces = []
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for face in combined_faces:
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if len(faces) == 0:
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faces.append(face)
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else:
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iou = [self.calculate_iou(face, f) for f in faces]
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if max(iou) < iou_threshold:
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faces.append(face)
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return faces
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def run(self, image, base_model, vae, positive_cond_base, negative_cond_base, seed, face_img_resolution=768, padding=8, scale_factor=1.2, min_neighbors=6, denoise=0.25,
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classifier='haarcascade_frontalface_default.xml', sampler_name='dpmpp_3m_sde_gpu', scheduler='exponential', cfg=7.0, steps=30):
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# tools
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image_scaler = ImageScale()
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vaeencoder = VAEEncode()
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vaedecoder = VAEDecode()
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# detect faces
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if classifier == 'combined':
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faces = self.combo_detection(image, scale_factor, min_neighbors)
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else:
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faces = self.detect_faces(image, classifier, scale_factor, min_neighbors)
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# if no faces detected
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if faces == ():
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return (image,)
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result = image.clone()
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# Draw rectangles around each face
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for (x, y, w, h) in faces:
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# factor in padding
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x -= padding
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y -= padding
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w += padding * 2
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h += padding * 2
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# Check if padded region is within bounds of the original image
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x = max(0, x)
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y = max(0, y)
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w = min(w, image.shape[2] - x)
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h = min(h, image.shape[1] - y)
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# crop face
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og_crop = image[:, y:y+h, x:x+w]
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# original size
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org_width, org_height = og_crop.shape[2], og_crop.shape[1]
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# upscale face
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crop = image_scaler.upscale(og_crop, 'lanczos', face_img_resolution, face_img_resolution, 'center')[0]
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samples = vaeencoder.encode(vae, crop)[0]
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samples = common_ksampler(base_model, seed, steps, cfg, sampler_name, scheduler, positive_cond_base, negative_cond_base, samples,
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start_step=int((1-(steps*denoise)) // 1), last_step=steps, force_full_denoise=False)[0]
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crop = vaedecoder.decode(vae, samples)[0]
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# resize face back to original size
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crop = image_scaler.upscale(crop, 'lanczos', org_width, org_height, 'center')[0]
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# calculate feather size
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feather = crop.shape[2] // 8
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# the image has 4 dimensions, 1st is the number of images in the batch, 2nd is the height, 3rd is the width, 4th is the number of channels
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mask = torch.ones(1, crop.shape[1], crop.shape[2], crop.shape[3])
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# feather on all sides
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# top feather
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for t in range(feather):
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mask[:, t:t+1, :] *= (1.0 / feather) * (t + 1)
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# left feather
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for t in range(feather):
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mask[:, :, t:t+1] *= (1.0 / feather) * (t + 1)
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# Right feather
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for t in range(feather):
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right_edge_start = crop.shape[2] - feather + t
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mask[:, :, right_edge_start:right_edge_start+1] *= (1.0 - (1.0 / feather) * (t + 1))
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# Bottom feather
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for t in range(feather):
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bottom_edge_start = crop.shape[1] - feather + t
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mask[:, bottom_edge_start:bottom_edge_start+1, :] *= (1.0 - (1.0 / feather) * (t + 1))
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# Apply the feathered mask to the cropped face
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crop = crop * mask
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# Extract the corresponding area on the original image
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original_area = result[:, y:y+h, x:x+w]
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# Apply inverse of the mask to the original area
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inverse_mask = 1 - mask
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original_area = original_area * inverse_mask
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# Add the processed face to the original area
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blended_face = original_area + crop
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# Place the blended face back into the result image
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result[:, y:y+h, x:x+w] = blended_face
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# Convert the result back to the original format if needed
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# (This step depends on how you want to return the image, adjust as necessary)
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# Return the final image
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return (result,)
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class PromptWithSDXL:
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@classmethod
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def INPUT_TYPES(s):
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@@ -4486,6 +4709,7 @@ NODE_CLASS_MAPPINGS = {
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'Mikey Sampler Base Only Advanced': MikeySamplerBaseOnlyAdvanced,
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'Mikey Sampler Tiled': MikeySamplerTiled,
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'Mikey Sampler Tiled Base Only': MikeySamplerTiledBaseOnly,
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'FaceFixerOpenCV': FaceFixerOpenCV,
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'AddMetaData': AddMetaData,
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'SaveMetaData': SaveMetaData,
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'SearchAndReplace': SearchAndReplace,
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@@ -4546,6 +4770,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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'MikeySamplerTiledAdvanced': 'Mikey Sampler Tiled Advanced',
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'MikeySamplerTiledAdvancedBaseOnly': 'Mikey Sampler Tiled Advanced Base Only',
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'Mikey Sampler Tiled Base Only': 'Mikey Sampler Tiled Base Only',
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'FaceFixerOpenCV': 'Face Fixer OpenCV (Mikey)',
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'AddMetaData': 'AddMetaData (Mikey)',
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'SaveMetaData': 'SaveMetaData (Mikey)',
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'SearchAndReplace': 'Search And Replace (Mikey)',
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