convert nodes_post_processing to V3 schema (#9491)

Author: bigcat88

comfy_extras/nodes_post_processing.py

@@ -1,3 +1,4 @@
+from typing_extensions import override
 import numpy as np
 import torch
 import torch.nn.functional as F
@@ -7,46 +8,41 @@
 import comfy.utils
 import comfy.model_management
 import node_helpers
+from comfy_api.latest import ComfyExtension, io
 
-class Blend:
-    def __init__(self):
-        pass
+class Blend(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageBlend",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image1"),
+                io.Image.Input("image2"),
+                io.Float.Input("blend_factor", default=0.5, min=0.0, max=1.0, step=0.01),
+                io.Combo.Input("blend_mode", options=["normal", "multiply", "screen", "overlay", "soft_light", "difference"]),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image1": ("IMAGE",),
-                "image2": ("IMAGE",),
-                "blend_factor": ("FLOAT", {
-                    "default": 0.5,
-                    "min": 0.0,
-                    "max": 1.0,
-                    "step": 0.01
-                }),
-                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "blend_images"
-
-    CATEGORY = "image/postprocessing"
-
-    def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
+    def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput:
         image1, image2 = node_helpers.image_alpha_fix(image1, image2)
         image2 = image2.to(image1.device)
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)
             image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
             image2 = image2.permute(0, 2, 3, 1)
 
-        blended_image = self.blend_mode(image1, image2, blend_mode)
+        blended_image = cls.blend_mode(image1, image2, blend_mode)
         blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
         blended_image = torch.clamp(blended_image, 0, 1)
-        return (blended_image,)
+        return io.NodeOutput(blended_image)
 
-    def blend_mode(self, img1, img2, mode):
+    @classmethod
+    def blend_mode(cls, img1, img2, mode):
         if mode == "normal":
             return img2
         elif mode == "multiply":
@@ -56,13 +52,13 @@ def blend_mode(self, img1, img2, mode):
         elif mode == "overlay":
             return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
         elif mode == "soft_light":
-            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (cls.g(img1) - img1))
         elif mode == "difference":
             return img1 - img2
-        else:
-            raise ValueError(f"Unsupported blend mode: {mode}")
+        raise ValueError(f"Unsupported blend mode: {mode}")
 
-    def g(self, x):
+    @classmethod
+    def g(cls, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
 def gaussian_kernel(kernel_size: int, sigma: float, device=None):
@@ -71,38 +67,26 @@ def gaussian_kernel(kernel_size: int, sigma: float, device=None):
     g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
     return g / g.sum()
 
-class Blur:
-    def __init__(self):
-        pass
+class Blur(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageBlur",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "blur_radius": ("INT", {
-                    "default": 1,
-                    "min": 1,
-                    "max": 31,
-                    "step": 1
-                }),
-                "sigma": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.1,
-                    "max": 10.0,
-                    "step": 0.1
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "blur"
-
-    CATEGORY = "image/postprocessing"
-
-    def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
+    def execute(cls, image: torch.Tensor, blur_radius: int, sigma: float) -> io.NodeOutput:
         if blur_radius == 0:
-            return (image,)
+            return io.NodeOutput(image)
 
         image = image.to(comfy.model_management.get_torch_device())
         batch_size, height, width, channels = image.shape
@@ -115,31 +99,24 @@ def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
         blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
         blurred = blurred.permute(0, 2, 3, 1)
 
-        return (blurred.to(comfy.model_management.intermediate_device()),)
+        return io.NodeOutput(blurred.to(comfy.model_management.intermediate_device()))
 
-class Quantize:
-    def __init__(self):
-        pass
 
+class Quantize(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "colors": ("INT", {
-                    "default": 256,
-                    "min": 1,
-                    "max": 256,
-                    "step": 1
-                }),
-                "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "quantize"
-
-    CATEGORY = "image/postprocessing"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageQuantize",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input("colors", default=256, min=1, max=256, step=1),
+                io.Combo.Input("dither", options=["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"]),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @staticmethod
     def bayer(im, pal_im, order):
@@ -167,7 +144,8 @@ def normalized_bayer_matrix(n):
         im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
         return im
 
-    def quantize(self, image: torch.Tensor, colors: int, dither: str):
+    @classmethod
+    def execute(cls, image: torch.Tensor, colors: int, dither: str) -> io.NodeOutput:
         batch_size, height, width, _ = image.shape
         result = torch.zeros_like(image)
 
@@ -187,46 +165,29 @@ def quantize(self, image: torch.Tensor, colors: int, dither: str):
             quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
             result[b] = quantized_array
 
-        return (result,)
+        return io.NodeOutput(result)
 
-class Sharpen:
-    def __init__(self):
-        pass
+class Sharpen(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageSharpen",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
+                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "sharpen_radius": ("INT", {
-                    "default": 1,
-                    "min": 1,
-                    "max": 31,
-                    "step": 1
-                }),
-                "sigma": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.1,
-                    "max": 10.0,
-                    "step": 0.01
-                }),
-                "alpha": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.0,
-                    "max": 5.0,
-                    "step": 0.01
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "sharpen"
-
-    CATEGORY = "image/postprocessing"
-
-    def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
+    def execute(cls, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float) -> io.NodeOutput:
         if sharpen_radius == 0:
-            return (image,)
+            return io.NodeOutput(image)
 
         batch_size, height, width, channels = image.shape
         image = image.to(comfy.model_management.get_torch_device())
@@ -245,23 +206,29 @@ def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha:
 
         result = torch.clamp(sharpened, 0, 1)
 
-        return (result.to(comfy.model_management.intermediate_device()),)
+        return io.NodeOutput(result.to(comfy.model_management.intermediate_device()))
 
-class ImageScaleToTotalPixels:
+class ImageScaleToTotalPixels(io.ComfyNode):
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
-                              "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
-                            }}
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "upscale"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageScaleToTotalPixels",
+            category="image/upscaling",
+            inputs=[
+                io.Image.Input("image"),
+                io.Combo.Input("upscale_method", options=cls.upscale_methods),
+                io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    CATEGORY = "image/upscaling"
-
-    def upscale(self, image, upscale_method, megapixels):
+    @classmethod
+    def execute(cls, image, upscale_method, megapixels) -> io.NodeOutput:
         samples = image.movedim(-1,1)
         total = int(megapixels * 1024 * 1024)
 
@@ -271,12 +238,18 @@ def upscale(self, image, upscale_method, megapixels):
 
         s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
         s = s.movedim(1,-1)
-        return (s,)
-
-NODE_CLASS_MAPPINGS = {
-    "ImageBlend": Blend,
-    "ImageBlur": Blur,
-    "ImageQuantize": Quantize,
-    "ImageSharpen": Sharpen,
-    "ImageScaleToTotalPixels": ImageScaleToTotalPixels,
-}
+        return io.NodeOutput(s)
+
+class PostProcessingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Blend,
+            Blur,
+            Quantize,
+            Sharpen,
+            ImageScaleToTotalPixels,
+        ]
+
+async def comfy_entrypoint() -> PostProcessingExtension:
+    return PostProcessingExtension()