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__init__.py

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+from .nodes import GradientBlurNode
+
+NODE_CLASS_MAPPINGS = {
+    "GradientBlur": GradientBlurNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "GradientBlur": "GradientBlur",
+}

nodes.py

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+import torch
+from torchvision.transforms.functional import gaussian_blur
+from comfy.cli_args import args
+from comfy.model_management import soft_empty_cache
+from comfy.utils import common_upscale
+from comfy_extras.nodes_upscale_model import *
+
+class GradientBlurNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "intensity": ("FLOAT", {"default": 10.0, "min": 0.0, "max": 100.0}),
+                "direction": (["custom", "top_to_bottom", "bottom_to_top", 
+                              "left_to_right", "right_to_left"],),
+                "auto_center": ("BOOLEAN", {"default": True}),
+                "center_x": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+                "center_y": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0}),
+                "sharp_edge": ("BOOLEAN", {"default": True}),
+                "bias": ("FLOAT", {"default": 0.0, "min": -1.0, "max": 1.0}),  # Новый параметр
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE")
+    RETURN_NAMES = ("image", "gradient_mask")
+    FUNCTION = "apply_gradient_blur"
+    CATEGORY = "image/blur"
+
+    def apply_gradient_blur(self, image, intensity, direction, auto_center, center_x, center_y, sharp_edge, bias):
+        if intensity <= 0:
+            return (image, image)
+
+        image = image.permute(0, 3, 1, 2)
+        batch_size, channels, height, width = image.shape
+        device = image.device
+
+        # Определение направления градиента
+        if direction == "custom":
+            if auto_center:
+                center_x = 0.5
+                center_y = 0.5
+        else:
+            pass  # Предопределённые направления игнорируют center_x/y
+
+        # Создание координатной сетки
+        x = torch.linspace(0, 1, width, device=device)
+        y = torch.linspace(0, 1, height, device=device)
+        grid_y, grid_x = torch.meshgrid(y, x, indexing='ij')
+
+        # Создание градиентной маски
+        if sharp_edge:
+            if direction == "top_to_bottom":
+                distance = grid_y
+            elif direction == "bottom_to_top":
+                distance = 1 - grid_y
+            elif direction == "left_to_right":
+                distance = grid_x
+            elif direction == "right_to_left":
+                distance = 1 - grid_x
+            elif direction == "custom":
+                distance = torch.sqrt((grid_x - center_x)**2 + (grid_y - center_y)**2)
+        else:
+            distance = torch.sqrt((grid_x - center_x)**2 + (grid_y - center_y)**2)
+
+        max_distance = distance.max()
+        mask = distance / max_distance
+        mask = torch.clamp(mask + bias, 0, 1)  # Применение bias
+
+        # Применение размытия
+        kernel_size = max(1, int(intensity) * 2 + 1)
+        blurred = gaussian_blur(image, kernel_size=kernel_size, sigma=(intensity, intensity))
+
+        # Интерполяция между оригинал и размытым
+        mask_expanded = mask.unsqueeze(0).unsqueeze(0).expand(batch_size, channels, height, width)
+        result = image * (1 - mask_expanded) + blurred * mask_expanded
+
+        # Преобразование маски для предпросмотра
+        gradient_preview = mask.unsqueeze(-1).repeat(1, 1, channels).cpu().numpy()
+        gradient_preview = torch.from_numpy(gradient_preview).unsqueeze(0).permute(0, 3, 1, 2)
+
+        # Возврат к исходному формату
+        result = result.permute(0, 2, 3, 1)
+        gradient_preview = gradient_preview.permute(0, 2, 3, 1)
+
+        return (result, gradient_preview)