diff --git a/README.md b/README.md
index 1341a58..caeae1b 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
   <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_NEW_EN.png?raw=true" alt="logo" width="180px"/>
 
-  ![Version](https://img.shields.io/badge/node_version-0.6.2_alpha4-lightgreen?style=for-the-badge&labelColor=darkgreen)
+  ![Version](https://img.shields.io/badge/node_version-0.6.2_beta1-green?style=for-the-badge&labelColor=darkgreen)
 
   <!--<sup>
   <font color=brightred>
@@ -54,6 +54,10 @@
 
 ## What's new in the latest update
 
+### 0.6.2 <sub><sup>BETA1</sup></sub>
+
+- Added support of HyperSwap models by FaceFusion Labs (thanks @Buumcode for contribution)<br>You can download them [here](https://huggingface.co/facefusion/models-3.3.0/tree/main)<br>(hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1a_256.onnx)<br>and put them into the `ComfyUI\models\hyperswap` directory
+
 ### 0.6.2 <sub><sup>ALPHA2, ALPHA3, ALPHA4</sup></sub>
 
 - Small but important fixes
@@ -260,6 +264,16 @@ Thanks to everyone who finds bugs, suggests new features and supports this proje
 
 </details>
 
+## Models
+
+ - buffalo_l: downloaded on first launch into `ComfyUI\models\insightface\models\buffalo_l`, or you can download manually from [here](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - inswapper_128: downloaded during installation into `ComfyUI\models\insightface`, or you can download manually from [here](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - reswapper_128/256: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models put them into `ComfyUI\models\reswapper`
+ - hyperswap_256: https://huggingface.co/facefusion/models-3.3.0/tree/main (hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1a_256.onnx) put them into `ComfyUI\models\hyperswap`
+ - Face restoration models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models put any you like into `ComfyUI\models\facerestore_models`
+ - Ultralytics model: https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt put into `ComfyUI\models\ultralytics\bbox`
+ - SAM models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/sams put into `ComfyUI\models\sams`
+
 ## Usage
 
 You can find ReActor Nodes inside the menu `ReActor` or by using a search (just type "ReActor" in the search field)
diff --git a/README_RU.md b/README_RU.md
index 29d1ca6..78a576b 100644
--- a/README_RU.md
+++ b/README_RU.md
@@ -2,7 +2,7 @@
 
   <img src="https://github.com/Gourieff/Assets/raw/main/sd-webui-reactor/ReActor_logo_NEW_RU.png?raw=true" alt="logo" width="180px"/>
 
-  ![Version](https://img.shields.io/badge/версия_нода-0.6.2_alpha4-lightgreen?style=for-the-badge&labelColor=darkgreen)
+  ![Version](https://img.shields.io/badge/версия_нода-0.6.2_beta1-green?style=for-the-badge&labelColor=darkgreen)
   
   <!--<sup>
   <font color=brightred>
@@ -54,6 +54,10 @@
 
 ## Что нового в последнем обновлении
 
+### 0.6.2 <sub><sup>BETA1</sup></sub>
+
+- Добавлена поддержка моделей HyperSwap от команды FaceFusion Labs (спасибо @Buumcode за вариант реализации)<br>Модели можно скачать [отсюда](https://huggingface.co/facefusion/models-3.3.0/tree/main)<br>(hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1a_256.onnx)<br>и положить в папку `ComfyUI/models/hyperswap`
+
 ### 0.6.2 <sub><sup>ALPHA2, ALPHA3, ALPHA4</sup></sub>
 
 - Небольшие, но важные исправления
@@ -262,6 +266,16 @@ Basic workflow [💾](https://github.com/Gourieff/Assets/blob/main/comfyui-react
 
 </details>
 
+## Модели
+
+ - buffalo_l: скачиваются при первом запуске в `ComfyUI\models\insightface\models\buffalo_l`, для ручного скачивания доступны [здесь](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - inswapper_128: скачивается при установке в `ComfyUI\models\insightface`, для ручного скачивания доступны [здесь](https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models)
+ - reswapper_128/256: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models скачать в `ComfyUI\models\reswapper`
+ - hyperswap_256: https://huggingface.co/facefusion/models-3.3.0/tree/main (hyperswap_1a_256.onnx, hyperswap_1b_256.onnx, hyperswap_1a_256.onnx) скачать в `ComfyUI\models\hyperswap`
+ - Face restoration models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/facerestore_models скачать любые предпочитаемые в `ComfyUI\models\facerestore_models`
+ - Ultralytics model: https://huggingface.co/datasets/Gourieff/ReActor/blob/main/models/detection/bbox/face_yolov8m.pt скачать в `ComfyUI\models\ultralytics\bbox`
+ - SAM models: https://huggingface.co/datasets/Gourieff/ReActor/tree/main/models/sams скачать в `ComfyUI\models\sams`
+
 <a name="usage">
 
 ## Использование
diff --git a/scripts/reactor_faceswap.py b/scripts/reactor_faceswap.py
index b969f0c..cf27d4c 100644
--- a/scripts/reactor_faceswap.py
+++ b/scripts/reactor_faceswap.py
@@ -26,7 +26,8 @@ import comfy.model_management as model_management
 def get_models():
     swappers = [
         "insightface",
-        "reswapper"
+        "reswapper",
+        "hyperswap"
     ]
     models_list = []
     for folder in swappers:
diff --git a/scripts/reactor_swapper.py b/scripts/reactor_swapper.py
index fc7d48a..2eda032 100644
--- a/scripts/reactor_swapper.py
+++ b/scripts/reactor_swapper.py
@@ -6,12 +6,10 @@ import cv2
 import numpy as np
 from PIL import Image
 
+import onnxruntime as ort
+
 import insightface
 from insightface.app.common import Face
-# try:
-#     import torch.cuda as cuda
-# except:
-#     cuda = None
 import torch
 
 import folder_paths
@@ -45,13 +43,6 @@ try:
 except Exception as e:
     logger.debug(f"ExecutionProviderError: {e}.\nEP is set to CPU.")
     providers = ["CPUExecutionProvider"]
-# if cuda is not None:
-#     if cuda.is_available():
-#         providers = ["CUDAExecutionProvider"]
-#     else:
-#         providers = ["CPUExecutionProvider"]
-# else:
-#     providers = ["CPUExecutionProvider"]
 
 models_path_old = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
 insightface_path_old = os.path.join(models_path_old, "insightface")
@@ -61,6 +52,7 @@ models_path = folder_paths.models_dir
 insightface_path = os.path.join(models_path, "insightface")
 insightface_models_path = os.path.join(insightface_path, "models")
 reswapper_path = os.path.join(models_path, "reswapper")
+hyperswap_path = os.path.join(models_path, "hyperswap")
 
 if os.path.exists(models_path_old):
     move_path(insightface_models_path_old, insightface_models_path)
@@ -122,11 +114,165 @@ def getFaceSwapModel(model_path: str):
     if FS_MODEL is None or CURRENT_FS_MODEL_PATH is None or CURRENT_FS_MODEL_PATH != model_path:
         CURRENT_FS_MODEL_PATH = model_path
         FS_MODEL = unload_model(FS_MODEL)
-        FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
+
+        model_filename = os.path.basename(model_path)
+        if "hyperswap" in model_filename.lower():
+            model_path = os.path.join(folder_paths.models_dir, "hyperswap", model_filename)
+            FS_MODEL = ort.InferenceSession(model_path, providers=providers)
+        elif "reswapper" in model_filename.lower():
+            model_path = os.path.join(folder_paths.models_dir, "reswapper", model_filename)
+            FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
+        else:
+            FS_MODEL = insightface.model_zoo.get_model(model_path, providers=providers)
 
     return FS_MODEL
 
 
+# Функция для получения 5 ключевых точек из объекта Face
+def get_landmarks_5(face):
+    if hasattr(face, 'landmark_5') and face.landmark_5 is not None:
+        return face.landmark_5
+    elif hasattr(face, 'kps') and face.kps is not None:
+        return face.kps
+    elif hasattr(face, 'landmark') and face.landmark is not None:
+        if face.landmark.shape[0] >= 68:
+            idxs = [36, 45, 30, 48, 54]
+            return face.landmark[idxs]
+    return None
+
+# Функция для вычисления аффинного преобразования
+def get_affine_transform(src_pts, dst_pts):
+    M, _ = cv2.estimateAffinePartial2D(src_pts, dst_pts)
+    return M
+
+# Создаём градиентную маску овальной формы без обрезки 
+def create_gradient_mask(crop_size=256):
+    # 1. Создаём пустую маску (все пиксели = 0)
+    mask = np.zeros((crop_size, crop_size), dtype=np.float32)
+    
+    # 2. Определяем центр и размеры эллипса
+    center = (crop_size // 2, crop_size // 2)
+    axes = (int(crop_size * 0.35), int(crop_size * 0.4))
+    
+    # 3. Рисуем эллипс (заполняем белым цветом, значение=1.0)
+    cv2.ellipse(
+        mask,          # Массив для рисования
+        center,        # Центр эллипса
+        axes,          # Полуоси (ширина, высота)
+        angle=0,       # Угол поворота
+        startAngle=0,  # Начальный угол дуги
+        endAngle=360,  # Конечный угол дуги (360 = полный эллипс)
+        color=1.0,     # Значение для заполнения (белый = 1.0)
+        thickness=-1   # -1 = заполнить всю область эллипса   
+    )
+    
+    # 4. Применяем размытие для плавных краёв
+    blur_ksize = 15  # Нечётное число, чтобы ядро было симметричным
+    mask = cv2.GaussianBlur(mask, (blur_ksize, blur_ksize), 0)
+    
+    # 5. Ограничим значения в диапазоне [0, 1]
+    mask = np.clip(mask, 0, 1)
+    
+    return mask
+
+def paste_back(target_img, swapped_face, M, crop_size=256):
+    
+    # 1. Создание мягкой маски (Эрозия + Размытие)
+    mask = create_gradient_mask(crop_size)
+
+    # Преобразуем в трехканальную маску
+    mask_3c = np.stack([mask] * 3, axis=2)
+
+    # 2. Получаем размеры целевого изображения
+    h, w = target_img.shape[:2]
+
+    # 3. Обратное преобразование (WARP_INVERSE_MAP) для лица И маски
+    # Для лица (INTER_LANCZOS4 — высококачественная интерполяция)
+    inv_face = cv2.warpAffine(
+        swapped_face.astype(np.float32),
+        M,
+        (w, h),
+        flags=cv2.INTER_LANCZOS4 | cv2.WARP_INVERSE_MAP,
+        borderMode=cv2.BORDER_TRANSPARENT
+    )
+
+    # Для маски (INTER_CUBIC — плавные границы)
+    inv_mask = cv2.warpAffine(
+        mask_3c,
+        M,
+        (w, h),
+        flags=cv2.INTER_CUBIC | cv2.WARP_INVERSE_MAP,
+        borderMode=cv2.BORDER_TRANSPARENT
+    )
+
+    # 4. Ограничение значений маски [0, 1]
+    inv_mask = np.clip(inv_mask, 0, 1)
+
+    # 5. Дополнительное размытие для устранения артефактов
+    inv_mask = cv2.GaussianBlur(inv_mask, (3, 3), 0)
+
+    # 6. Плавное наложение
+    target_img_float = target_img.astype(np.float32)
+    inv_face_float = inv_face.astype(np.float32)
+    result = target_img_float * (1.0 - inv_mask) + inv_face_float * inv_mask
+
+    # 7. Ограничение результата [0, 255]
+    result = np.clip(result, 0, 255).astype(np.uint8)
+
+    return result
+
+def visualize_points(img, points, color=(0, 255, 0)):
+    img = img.copy()
+    for p in points:
+        cv2.circle(img, tuple(p.astype(int)), 3, color, -1)
+
+# Итоговая функция run_hyperswap с аффинным преобразованием
+def run_hyperswap(session, source_face, target_face, target_img):
+    # 1. Подготовка эмбеддинга
+    source_embedding = source_face.normed_embedding.reshape(1, -1).astype(np.float32)
+
+    # 2. Получаем 5 точек target
+    target_landmarks_5 = get_landmarks_5(target_face)
+    visualize_points(target_img, target_landmarks_5, (0, 255, 0))  # Зеленые точки
+    
+    if target_landmarks_5 is None:
+        return None, None
+
+    # 3. Определение эталонных точек для выравнивания 256x256 (FFHQ Alignment)
+    std_landmarks_256 = np.array([
+        [ 84.87, 105.94],  # Левый глаз
+        [171.13, 105.94],  # Правый глаз
+        [128.00, 146.66],  # Кончик носа
+        [ 96.95, 188.64],  # Левый уголок рта
+        [159.05, 188.64]   # Правый уголок рта
+    ], dtype=np.float32)
+
+    # Вычисляем аффинную матрицу
+    M = get_affine_transform(target_landmarks_5.astype(np.float32), std_landmarks_256)
+    
+    # Применяем аффинное преобразование с новой матрицей M
+    crop = cv2.warpAffine(target_img, M, (256, 256), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REFLECT)
+
+    # 4. Преобразуем crop для модели
+    crop_input = crop[:, :, ::-1].astype(np.float32) / 255.0  # RGB -> [0,1]
+    crop_input = (crop_input - 0.5) / 0.5  # Нормализация
+    crop_input = crop_input.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32)
+
+    # 5. Инференс
+    try:
+        output = session.run(None, {'source': source_embedding, 'target': crop_input})[0][0]
+    except:
+        return target_img
+
+    # 6. Обратная нормализация
+    output = (output * 0.5 + 0.5) * 255.0  # [-1..1] -> [0..255]
+    output = np.clip(output, 0, 255).astype(np.uint8)
+    output = output.transpose(1, 2, 0)  # CHW -> HWC
+    output = output[:, :, ::-1]  # BGR -> RGB
+    
+    return output, M # Возвращаем лицо (256x256) и матрицу M
+
+
 def sort_by_order(face, order: str):
     if order == "left-right":
         return sorted(face, key=lambda x: x.bbox[0])
@@ -160,7 +306,7 @@ def get_face_gender(
     if len(filtered_faces) == 0:
         if gender_condition != 0:
             logger.status(f"No faces found for -{gender}-")
-        return None, 0  # treat as "wrong gender" to skip
+        return None, 0, None  # treat as "wrong gender" to skip
 
     faces_sorted = sort_by_order(filtered_faces, order)
 
@@ -346,6 +492,9 @@ def swap_face(
                     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
@@ -364,8 +513,12 @@ def swap_face(
                         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])
                         if target_face is not None and wrong_gender == 0:
                             logger.status(f"Swapping...")
-                            if face_boost_enabled:
-                                logger.status(f"Face Boost is enabled")
+                            if "hyperswap" in model:
+                                swapped_face_256, M = run_hyperswap(face_swapper, source_face, target_face, result)
+                                if swapped_face_256 is not None:
+                                    result = paste_back(result, swapped_face_256, M, crop_size=256)
+                            elif face_boost_enabled:
+                                logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
                                 bgr_fake, M = face_swapper.get(result, target_face, source_face, paste_back=False)
                                 bgr_fake, scale = restorer.get_restored_face(bgr_fake, face_restore_model, face_restore_visibility, codeformer_weight, interpolation)
                                 M *= scale
@@ -541,7 +694,13 @@ def swap_face_many(
                 logger.status(f'Source Faces must have no entries (default=0), one entry, or same number of entries as target faces.')
             elif source_face is not None:
                 results = target_imgs
-                model_path = model_path = os.path.join(insightface_path, model)
+                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
@@ -566,14 +725,17 @@ def swap_face_many(
                             target_face_single, wrong_gender, target_face_index = get_face_single(target_img, target_face, face_index=face_num, gender_target=gender_target, order=faces_order[0])
                             if target_face_single is not None and wrong_gender == 0:
                                 result = target_img
-                                if face_boost_enabled:
-                                    logger.status(f"Face Boost is enabled")
+                                if "hyperswap" in model:
+                                    swapped_face_256, M = run_hyperswap(face_swapper, source_face, target_face_single, result)
+                                    if swapped_face_256 is not None:
+                                        result = paste_back(result, swapped_face_256, M, crop_size=256)
+                                elif face_boost_enabled:
+                                    logger.status(f"Face Boost is enabled (inswapper/reswapper only)")
                                     bgr_fake, M = face_swapper.get(target_img, target_face_single, source_face, 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:
-                                    # logger.status(f"Swapping as-is")
                                     result = face_swapper.get(target_img, target_face_single, source_face)
                                 results[i] = result
                                 bbox.append(tuple(map(float, target_face_single.bbox)))
diff --git a/scripts/reactor_version.py b/scripts/reactor_version.py
index e2ddcaa..3fba4e8 100644
--- a/scripts/reactor_version.py
+++ b/scripts/reactor_version.py
@@ -1,5 +1,5 @@
 app_title = "ReActor Node for ComfyUI"
-version_flag = "v0.6.2-a4"
+version_flag = "v0.6.2-b1"
 
 COLORS = {
     "CYAN": "\033[0;36m",  # CYAN