582 lines
25 KiB
Python
582 lines
25 KiB
Python
import os
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import shutil
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from typing import List, Union
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import cv2
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import numpy as np
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from PIL import Image
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from reactor_core.analyzer import ReActorFaceAnalysis
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from reactor_core.face_objects import Face
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from reactor_core.inswap import INSwapper
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from reactor_core.hyperswap import HyperSwapper
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import torch
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import folder_paths
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import comfy.model_management as model_management
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from r_modules.shared import state
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from scripts.reactor_logger import logger
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from reactor_utils import (
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move_path,
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get_image_md5hash,
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progress_bar,
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progress_bar_reset
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)
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from scripts.r_faceboost import swapper, restorer
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import warnings
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np.warnings = warnings
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np.warnings.filterwarnings('ignore')
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# PROVIDERS
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try:
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if torch.cuda.is_available():
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providers = ["CUDAExecutionProvider"]
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elif torch.backends.mps.is_available():
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providers = ["CoreMLExecutionProvider"]
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elif hasattr(torch,'dml') or hasattr(torch,'privateuseone'):
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providers = ["ROCMExecutionProvider"]
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else:
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providers = ["CPUExecutionProvider"]
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except Exception as e:
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logger.debug(f"ExecutionProviderError: {e}.\nEP is set to CPU.")
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providers = ["CPUExecutionProvider"]
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models_path_old = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
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insightface_path_old = os.path.join(models_path_old, "insightface")
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insightface_models_path_old = os.path.join(insightface_path_old, "models")
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models_path = folder_paths.models_dir
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insightface_path = os.path.join(models_path, "insightface")
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insightface_models_path = os.path.join(insightface_path, "models")
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reswapper_path = os.path.join(models_path, "reswapper")
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hyperswap_path = os.path.join(models_path, "hyperswap")
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if os.path.exists(models_path_old):
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move_path(insightface_models_path_old, insightface_models_path)
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move_path(insightface_path_old, insightface_path)
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move_path(models_path_old, models_path)
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if os.path.exists(insightface_path) and os.path.exists(insightface_path_old):
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shutil.rmtree(insightface_path_old)
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shutil.rmtree(models_path_old)
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FS_MODEL = None
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CURRENT_FS_MODEL_PATH = None
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ANALYSIS_MODELS = {
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"640": None,
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"320": None,
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}
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SOURCE_FACES = None
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SOURCE_IMAGE_HASH = None
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TARGET_FACES = None
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TARGET_IMAGE_HASH = None
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TARGET_FACES_LIST = []
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TARGET_IMAGE_LIST_HASH = []
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def unload_model(model):
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if model is not None:
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del model
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return None
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def unload_all_models():
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global FS_MODEL, CURRENT_FS_MODEL_PATH
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FS_MODEL = unload_model(FS_MODEL)
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ANALYSIS_MODELS["320"] = unload_model(ANALYSIS_MODELS["320"])
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ANALYSIS_MODELS["640"] = unload_model(ANALYSIS_MODELS["640"])
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def get_current_faces_model():
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global SOURCE_FACES
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return SOURCE_FACES
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def getAnalysisModel(det_size = (640, 640)):
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global ANALYSIS_MODELS
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ANALYSIS_MODEL = ANALYSIS_MODELS[str(det_size[0])]
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if ANALYSIS_MODEL is None:
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ANALYSIS_MODEL = ReActorFaceAnalysis(
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name="buffalo_l", providers=providers, root=insightface_path
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)
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ANALYSIS_MODEL.prepare(ctx_id=0, det_size=det_size)
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ANALYSIS_MODELS[str(det_size[0])] = ANALYSIS_MODEL
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return ANALYSIS_MODEL
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def getFaceSwapModel(model_path: str):
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global FS_MODEL, CURRENT_FS_MODEL_PATH
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if FS_MODEL is None or CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
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CURRENT_FS_MODEL_PATH = model_path
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FS_MODEL = unload_model(FS_MODEL)
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model_filename = os.path.basename(model_path)
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if "hyperswap" in model_filename.lower(): # Если это Hyperswap
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model_path = os.path.join(folder_paths.models_dir, "hyperswap", model_filename)
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FS_MODEL = HyperSwapper(model_path, providers=providers)
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else: # Если это INSwapper / Reswapper
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if "reswapper" in model_filename.lower():
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model_path = os.path.join(folder_paths.models_dir, "reswapper", model_filename)
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FS_MODEL = INSwapper(model_path, providers=providers)
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return FS_MODEL
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def sort_by_order(face, order: str):
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if order == "left-right":
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return sorted(face, key=lambda x: x.bbox[0])
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if order == "right-left":
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return sorted(face, key=lambda x: x.bbox[0], reverse = True)
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if order == "top-bottom":
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return sorted(face, key=lambda x: x.bbox[1])
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if order == "bottom-top":
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return sorted(face, key=lambda x: x.bbox[1], reverse = True)
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if order == "small-large":
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return sorted(face, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))
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# by default "large-small":
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return sorted(face, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]), reverse = True)
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def get_face_gender(
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face,
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face_index,
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gender_condition,
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operated: str,
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order: str,
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):
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# 1. Сортируем ВСЕ найденные лица (без фильтрации!)
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faces_sorted = sort_by_order(face, order)
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# 2. Проверяем, существует ли вообще лицо с таким визуальным индексом
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if face_index >= len(faces_sorted):
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logger.info("Requested face index (%s) is out of bounds (max available index is %s)", face_index, len(faces_sorted) - 1)
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return None, 0, None
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# 3. Берем конкретное лицо по его позиции на фото (например, второе справа)
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face_selected = faces_sorted[face_index]
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# Если фильтр по полу отключен (no) - сразу отдаем лицо в работу
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if gender_condition == 0:
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return face_selected, 0, face_index
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# 4. Проверяем пол выбранного лица
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# face.gender: 0 = female, 1 = male
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# gender_condition: 1 = female, 2 = male
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expected_gender = 0 if gender_condition == 1 else 1
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actual_gender = getattr(face_selected, 'gender', -1)
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sel_gender_str = "Male" if actual_gender == 1 else "Female" if actual_gender == 0 else "Unknown"
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logger.info("%s Face %s: Detected Gender -%s-", operated, face_index, sel_gender_str)
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# Если пол не совпадает с тем, что заказал юзер
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if actual_gender != expected_gender:
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logger.info(f"{operated} Face {face_index}: WRONG gender ({sel_gender_str})")
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return face_selected, 1, face_index # 1 означает флаг wrong_gender = True (цикл его пропустит)
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# Если всё идеально
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return face_selected, 0, face_index
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def half_det_size(det_size):
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logger.status("Trying to halve 'det_size' parameter")
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return (det_size[0] // 2, det_size[1] // 2)
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def analyze_faces(img_data: np.ndarray, det_size=(640, 640)):
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face_analyser = getAnalysisModel(det_size)
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faces = []
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try:
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faces = face_analyser.get(img_data)
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except Exception as e:
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# import traceback
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# traceback.print_exc()
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# logger.error(f"Error during face analysis: {e}")
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logger.error("No faces found")
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# Try halving det_size if no faces are found
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if len(faces) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return analyze_faces(img_data, det_size_half)
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return faces
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def get_face_single(img_data: np.ndarray, face, face_index=0, det_size=(640, 640), gender_source=0, gender_target=0, order="large-small"):
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buffalo_path = os.path.join(insightface_models_path, "buffalo_l.zip")
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if os.path.exists(buffalo_path):
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os.remove(buffalo_path)
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if gender_source != 0:
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target, order)
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return get_face_gender(face,face_index,gender_source,"Source", order)
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if gender_target != 0:
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target, order)
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return get_face_gender(face,face_index,gender_target,"Target", order)
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if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
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det_size_half = half_det_size(det_size)
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return get_face_single(img_data, analyze_faces(img_data, det_size_half), face_index, det_size_half, gender_source, gender_target, order)
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try:
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faces_sorted = sort_by_order(face, order)
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return faces_sorted[face_index], 0, face_index
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except IndexError:
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return None, 0, None
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def swap_face(
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source_img: Union[Image.Image, None],
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target_img: Image.Image,
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model: Union[str, None] = None,
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source_faces_index: List[int] = [0],
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faces_index: List[int] = [0],
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gender_source: int = 0,
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gender_target: int = 0,
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face_model: Union[Face, None] = None,
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faces_order: List = ["large-small", "large-small"],
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face_boost_enabled: bool = False,
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face_restore_model = None,
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face_restore_visibility: int = 1,
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codeformer_weight: float = 0.5,
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interpolation: str = "Bicubic",
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):
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global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH
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result_image = target_img
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bbox = []
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swapped_indexes = []
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if model is not None:
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if isinstance(source_img, str): # source_img is a base64 string
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import base64, io
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if 'base64,' in source_img: # check if the base64 string has a data URL scheme
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# split the base64 string to get the actual base64 encoded image data
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base64_data = source_img.split('base64,')[-1]
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# decode base64 string to bytes
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img_bytes = base64.b64decode(base64_data)
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else:
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# if no data URL scheme, just decode
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img_bytes = base64.b64decode(source_img)
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source_img = Image.open(io.BytesIO(img_bytes))
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target_img = cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR)
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if source_img is not None:
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source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
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source_image_md5hash = get_image_md5hash(source_img)
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if SOURCE_IMAGE_HASH is None:
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SOURCE_IMAGE_HASH = source_image_md5hash
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source_image_same = False
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else:
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source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
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if not source_image_same:
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SOURCE_IMAGE_HASH = source_image_md5hash
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logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
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logger.info("Source Image the Same? %s", source_image_same)
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if SOURCE_FACES is None or not source_image_same:
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logger.status("Analyzing Source Image...")
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source_faces = analyze_faces(source_img)
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SOURCE_FACES = source_faces
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elif source_image_same:
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logger.status("Using Hashed Source Face(s) Model...")
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source_faces = SOURCE_FACES
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elif face_model is not None:
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source_faces_index = [0]
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logger.status("Using Loaded Source Face Model...")
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source_face_model = [face_model]
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source_faces = source_face_model
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else:
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logger.error("Cannot detect any Source")
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if source_faces is not None:
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target_image_md5hash = get_image_md5hash(target_img)
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if TARGET_IMAGE_HASH is None:
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TARGET_IMAGE_HASH = target_image_md5hash
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target_image_same = False
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else:
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target_image_same = True if TARGET_IMAGE_HASH == target_image_md5hash else False
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if not target_image_same:
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TARGET_IMAGE_HASH = target_image_md5hash
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logger.info("Target Image MD5 Hash = %s", TARGET_IMAGE_HASH)
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logger.info("Target Image the Same? %s", target_image_same)
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if TARGET_FACES is None or not target_image_same:
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logger.status("Analyzing Target Image...")
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target_faces = analyze_faces(target_img)
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TARGET_FACES = target_faces
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elif target_image_same:
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logger.status("Using Hashed Target Face(s) Model...")
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target_faces = TARGET_FACES
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if len(target_faces) == 0:
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logger.status("Cannot detect any Target, skipping swapping...")
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return result_image, bbox, swapped_indexes
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# --- НОВАЯ ИДЕАЛЬНАЯ ЛОГИКА СОРТИРОВКИ ---
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# 1. Заранее собираем список ТОЛЬКО ВАЛИДНЫХ исходных лиц
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valid_source_faces = []
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if source_img is not None:
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for idx in source_faces_index:
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sf, src_wrong_gender, _ = get_face_single(source_img, source_faces, face_index=idx, gender_source=gender_source, order=faces_order[1])
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if sf is not None and src_wrong_gender == 0:
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valid_source_faces.append(sf)
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else:
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sf, src_wrong_gender, _ = get_face_single(None, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
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if sf is not None and src_wrong_gender == 0:
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valid_source_faces.append(sf)
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if len(valid_source_faces) == 0:
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logger.status("No valid source face(s) found in the provided Index after gender filter")
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else:
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result = target_img
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if "inswapper" in model:
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model_path = os.path.join(insightface_path, model)
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elif "reswapper" in model:
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model_path = os.path.join(reswapper_path, model)
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elif "hyperswap" in model:
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model_path = os.path.join(hyperswap_path, model)
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face_swapper = getFaceSwapModel(model_path)
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source_face_idx = 0
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# 2. Идем по целевым лицам
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for face_num in faces_index:
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target_face, wrong_gender, target_face_index = get_face_single(target_img, target_faces, face_index=face_num, gender_target=gender_target, order=faces_order[0])
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if target_face is not None and wrong_gender == 0:
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logger.status(f"Swapping...")
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# 3. Берем валидное лицо (если их меньше, чем целей — идем по кругу)
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source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
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if face_boost_enabled and "hyperswap" not in model:
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logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
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bgr_fake, M = face_swapper.get(result, target_face, source_face_to_use, paste_back=False)
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bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
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M *= scale
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result = swapper.in_swap(result, bgr_fake, M)
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else:
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result = face_swapper.get(result, target_face, source_face_to_use)
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bbox.append(tuple(map(float, target_face.bbox)))
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swapped_indexes.append(target_face_index)
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# Продвигаем индекс исходного лица ТОЛЬКО после УСПЕШНОГО применения
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if len(valid_source_faces) > 1:
|
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source_face_idx += 1
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|
|
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elif wrong_gender == 1:
|
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logger.status("Wrong target gender detected")
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continue
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else:
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logger.info(f"No target face found for {face_num}")
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|
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result_image = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
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|
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else:
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logger.status("No source face(s) found")
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return result_image, bbox, swapped_indexes
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|
|
def swap_face_many(
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source_img: Union[Image.Image, None],
|
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target_imgs: List[Image.Image],
|
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model: Union[str, None] = None,
|
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source_faces_index: List[int] = [0],
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faces_index: List[int] = [0],
|
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gender_source: int = 0,
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gender_target: int = 0,
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face_model: Union[Face, None] = None,
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faces_order: List = ["large-small", "large-small"],
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face_boost_enabled: bool = False,
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face_restore_model = None,
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face_restore_visibility: int = 1,
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codeformer_weight: float = 0.5,
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interpolation: str = "Bicubic",
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):
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global SOURCE_FACES, SOURCE_IMAGE_HASH, TARGET_FACES, TARGET_IMAGE_HASH, TARGET_FACES_LIST, TARGET_IMAGE_LIST_HASH
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result_images = target_imgs
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bbox = []
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swapped_indexes = []
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|
|
|
if model is not None:
|
|
if isinstance(source_img, str):
|
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import base64, io
|
|
if 'base64,' in source_img:
|
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base64_data = source_img.split('base64,')[-1]
|
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img_bytes = base64.b64decode(base64_data)
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|
else:
|
|
img_bytes = base64.b64decode(source_img)
|
|
source_img = Image.open(io.BytesIO(img_bytes))
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|
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target_imgs = [cv2.cvtColor(np.array(target_img), cv2.COLOR_RGB2BGR) for target_img in target_imgs]
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|
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if source_img is not None:
|
|
source_img = cv2.cvtColor(np.array(source_img), cv2.COLOR_RGB2BGR)
|
|
source_image_md5hash = get_image_md5hash(source_img)
|
|
|
|
if SOURCE_IMAGE_HASH is None:
|
|
SOURCE_IMAGE_HASH = source_image_md5hash
|
|
source_image_same = False
|
|
else:
|
|
source_image_same = True if SOURCE_IMAGE_HASH == source_image_md5hash else False
|
|
if not source_image_same:
|
|
SOURCE_IMAGE_HASH = source_image_md5hash
|
|
|
|
logger.info("Source Image MD5 Hash = %s", SOURCE_IMAGE_HASH)
|
|
logger.info("Source Image the Same? %s", source_image_same)
|
|
|
|
if SOURCE_FACES is None or not source_image_same:
|
|
logger.status("Analyzing Source Image...")
|
|
source_faces = analyze_faces(source_img)
|
|
SOURCE_FACES = source_faces
|
|
elif source_image_same:
|
|
logger.status("Using Hashed Source Face(s) Model...")
|
|
source_faces = SOURCE_FACES
|
|
|
|
elif face_model is not None:
|
|
source_faces_index = [0]
|
|
logger.status("Using Loaded Source Face Model...")
|
|
source_face_model = [face_model]
|
|
source_faces = source_face_model
|
|
else:
|
|
logger.error("Cannot detect any Source")
|
|
|
|
if source_faces is not None:
|
|
target_faces = []
|
|
pbar = progress_bar(len(target_imgs))
|
|
|
|
if len(TARGET_IMAGE_LIST_HASH) > 0:
|
|
logger.status(f"Using Hashed Target Face(s) Model...")
|
|
else:
|
|
logger.status(f"Analyzing Target Image...")
|
|
|
|
for i, target_img in enumerate(target_imgs):
|
|
if state.interrupted or model_management.processing_interrupted():
|
|
logger.status("Interrupted by User")
|
|
break
|
|
|
|
target_image_md5hash = get_image_md5hash(target_img)
|
|
if len(TARGET_IMAGE_LIST_HASH) == 0:
|
|
TARGET_IMAGE_LIST_HASH = [target_image_md5hash]
|
|
target_image_same = False
|
|
elif len(TARGET_IMAGE_LIST_HASH) == i:
|
|
TARGET_IMAGE_LIST_HASH.append(target_image_md5hash)
|
|
target_image_same = False
|
|
else:
|
|
target_image_same = True if TARGET_IMAGE_LIST_HASH[i] == target_image_md5hash else False
|
|
if not target_image_same:
|
|
TARGET_IMAGE_LIST_HASH[i] = target_image_md5hash
|
|
|
|
logger.info("(Image %s) Target Image MD5 Hash = %s", i, TARGET_IMAGE_LIST_HASH[i])
|
|
logger.info("(Image %s) Target Image the Same? %s", i, target_image_same)
|
|
|
|
if len(TARGET_FACES_LIST) == 0:
|
|
target_face = analyze_faces(target_img)
|
|
TARGET_FACES_LIST = [target_face]
|
|
elif len(TARGET_FACES_LIST) == i and not target_image_same:
|
|
target_face = analyze_faces(target_img)
|
|
TARGET_FACES_LIST.append(target_face)
|
|
elif len(TARGET_FACES_LIST) != i and not target_image_same:
|
|
target_face = analyze_faces(target_img)
|
|
TARGET_FACES_LIST[i] = target_face
|
|
elif target_image_same:
|
|
target_face = TARGET_FACES_LIST[i]
|
|
|
|
if target_face is not None:
|
|
target_faces.append(target_face)
|
|
pbar.update(1)
|
|
|
|
progress_bar_reset(pbar)
|
|
|
|
if len(target_faces) == 0:
|
|
logger.status("Cannot detect any Target, skipping swapping...")
|
|
return result_images, bbox, swapped_indexes
|
|
|
|
# --- НОВАЯ ИДЕАЛЬНАЯ ЛОГИКА СОРТИРОВКИ ---
|
|
|
|
valid_source_faces = []
|
|
if source_img is not None:
|
|
for idx in source_faces_index:
|
|
sf, src_wrong_gender, _ = get_face_single(source_img, source_faces, face_index=idx, gender_source=gender_source, order=faces_order[1])
|
|
if sf is not None and src_wrong_gender == 0:
|
|
valid_source_faces.append(sf)
|
|
else:
|
|
sf, src_wrong_gender, _ = get_face_single(None, source_faces, face_index=source_faces_index[0], gender_source=gender_source, order=faces_order[1])
|
|
if sf is not None and src_wrong_gender == 0:
|
|
valid_source_faces.append(sf)
|
|
|
|
if len(valid_source_faces) == 0:
|
|
logger.status("No valid source face(s) found in the provided Index after gender filter")
|
|
else:
|
|
results = target_imgs
|
|
if "inswapper" in model:
|
|
model_path = os.path.join(insightface_path, model)
|
|
elif "reswapper" in model:
|
|
model_path = os.path.join(reswapper_path, model)
|
|
elif "hyperswap" in model:
|
|
model_path = os.path.join(hyperswap_path, model)
|
|
|
|
face_swapper = getFaceSwapModel(model_path)
|
|
|
|
source_face_idx = 0
|
|
pbar = progress_bar(len(target_imgs))
|
|
logger.status(f"Swapping...")
|
|
|
|
for face_num in faces_index:
|
|
target_used_in_any_image = False
|
|
|
|
for i, (target_img, target_face_list) in enumerate(zip(results, target_faces)):
|
|
target_face_single, wrong_gender, target_face_index = get_face_single(target_img, target_face_list, face_index=face_num, gender_target=gender_target, order=faces_order[0])
|
|
|
|
if target_face_single is not None and wrong_gender == 0:
|
|
target_used_in_any_image = True
|
|
source_face_to_use = valid_source_faces[source_face_idx % len(valid_source_faces)]
|
|
|
|
result = target_img
|
|
if face_boost_enabled and "hyperswap" not in model:
|
|
bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face_to_use, paste_back=False)
|
|
bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
|
|
M *= scale
|
|
result = swapper.in_swap(target_img, bgr_fake, M)
|
|
else:
|
|
result = face_swapper.get(target_img, target_face_single, source_face_to_use)
|
|
|
|
results[i] = result
|
|
bbox.append(tuple(map(float, target_face_single.bbox)))
|
|
swapped_indexes.append(target_face_index)
|
|
pbar.update(1)
|
|
|
|
elif wrong_gender == 1:
|
|
logger.status("Wrong target gender detected")
|
|
pbar.update(1)
|
|
continue
|
|
else:
|
|
logger.info(f"{i}: No target face found for {face_num}")
|
|
pbar.update(1)
|
|
|
|
if target_used_in_any_image and len(valid_source_faces) > 1:
|
|
source_face_idx += 1
|
|
|
|
progress_bar_reset(pbar)
|
|
result_images = [Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB)) for result in results]
|
|
|
|
else:
|
|
logger.status("No source face(s) found")
|
|
return result_images, bbox, swapped_indexes
|