fixes, corrections; ported MaskPreview, WebcamCapture and LoadImageOutput nodes

comfy_api/v3/io.py

@@ -209,8 +209,6 @@ class WidgetInputV3(InputV3):
         })
     
     def get_io_type_V1(self):
-        if isinstance(self, Combo.Input):
-            return self.as_value_type_v1()
         return self.widgetType if self.widgetType is not None else super().get_io_type_V1()
 
 
@@ -411,18 +409,7 @@ class Combo(ComfyType):
             self.remote = remote
             self.default: str
 
-        def as_dict_V1(self):
-            return super().as_dict_V1() | prune_dict({
-                "multiselect": self.multiselect,
-                "options": self.options,
-                "control_after_generate": self.control_after_generate,
-                "image_upload": self.image_upload,
-                "image_folder": self.image_folder.value if self.image_folder else None,
-                "content_types": self.content_types if self.content_types else None,
-                "remote": self.remote.as_dict() if self.remote else None,
-            })
-
-        def as_value_type_v1(self):
+        def get_io_type_V1(self):
             if getattr(self, "image_folder"):
                 if self.image_folder == FolderType.input:
                     target_dir = folder_paths.get_input_directory()
@@ -434,6 +421,18 @@ class Combo(ComfyType):
                 if self.content_types is None:
                     return files
                 return sorted(folder_paths.filter_files_content_types(files, self.content_types))
+            return super().get_io_type_V1()
+
+        def as_dict_V1(self):
+            return super().as_dict_V1() | prune_dict({
+                "multiselect": self.multiselect,
+                "options": self.options,
+                "control_after_generate": self.control_after_generate,
+                "image_upload": self.image_upload,
+                "image_folder": self.image_folder.value if self.image_folder else None,
+                "content_types": self.content_types if self.content_types else None,
+                "remote": self.remote.as_dict() if self.remote else None,
+            })
 
 
 @comfytype(io_type="COMBO")
@@ -463,6 +462,20 @@ class MultiCombo(ComfyType):
 class Image(ComfyTypeIO):
     Type = torch.Tensor
 
+@comfytype(io_type="WEBCAM")
+class Webcam(ComfyTypeIO):
+    Type = str
+
+    class Input(WidgetInputV3):
+        """Webcam input."""
+        Type = str
+        def __init__(
+                self, id: str, display_name: str=None, optional=False,
+                tooltip: str=None, lazy: bool=None, default: str=None, socketless: bool=None
+        ):
+            super().__init__(id, display_name, optional, tooltip, lazy, default, socketless, self.io_type)
+
+
 @comfytype(io_type="MASK")
 class Mask(ComfyTypeIO):
     Type = torch.Tensor
@@ -1121,7 +1134,7 @@ class ComfyNodeV3:
         type_clone: type[ComfyNodeV3] = type(f"CLEAN_{c_type.__name__}", c_type.__bases__, {})
         # TODO: what parameters should be carried over?
         type_clone.SCHEMA = c_type.SCHEMA
-        type_clone.hidden = HiddenHolder.from_dict(hidden_inputs)
+        type_clone.hidden = HiddenHolder.from_dict(hidden_inputs) if hidden_inputs is not None else None
         # TODO: add anything we would want to expose inside node's execute function
         return type_clone
 

comfy_extras/nodes_mask.py

@@ -3,10 +3,7 @@ import scipy.ndimage
 import torch
 import comfy.utils
 import node_helpers
-import folder_paths
-import random
 
-import nodes
 from nodes import MAX_RESOLUTION
 
 def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
@@ -365,30 +362,6 @@ class ThresholdMask:
         mask = (mask > value).float()
         return (mask,)
 
-# Mask Preview - original implement from
-# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
-# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
-class MaskPreview(nodes.SaveImage):
-    def __init__(self):
-        self.output_dir = folder_paths.get_temp_directory()
-        self.type = "temp"
-        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
-        self.compress_level = 4
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {"mask": ("MASK",), },
-            "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-        }
-
-    FUNCTION = "execute"
-    CATEGORY = "mask"
-
-    def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
-        return self.save_images(preview, filename_prefix, prompt, extra_pnginfo)
-
 
 NODE_CLASS_MAPPINGS = {
     "LatentCompositeMasked": LatentCompositeMasked,
@@ -403,10 +376,8 @@ NODE_CLASS_MAPPINGS = {
     "FeatherMask": FeatherMask,
     "GrowMask": GrowMask,
     "ThresholdMask": ThresholdMask,
-    "MaskPreview": MaskPreview
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageToMask": "Convert Image to Mask",
-    "MaskToImage": "Convert Mask to Image",
 }

comfy_extras/nodes_webcam.py

@@ -1,37 +0,0 @@
-import nodes
-import folder_paths
-
-MAX_RESOLUTION = nodes.MAX_RESOLUTION
-
-
-class WebcamCapture(nodes.LoadImage):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("WEBCAM", {}),
-                "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
-                "capture_on_queue": ("BOOLEAN", {"default": True}),
-            }
-        }
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "load_capture"
-
-    CATEGORY = "image"
-
-    def load_capture(self, image, **kwargs):
-        return super().load_image(folder_paths.get_annotated_filepath(image))
-
-    @classmethod
-    def IS_CHANGED(cls, image, width, height, capture_on_queue):
-        return super().IS_CHANGED(image)
-
-
-NODE_CLASS_MAPPINGS = {
-    "WebcamCapture": WebcamCapture,
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "WebcamCapture": "Webcam Capture",
-}

comfy_extras/v3/nodes_images.py

@@ -0,0 +1,283 @@
+import json
+import os
+import torch
+import hashlib
+
+import numpy as np
+from PIL import Image, ImageOps, ImageSequence
+from PIL.PngImagePlugin import PngInfo
+
+from comfy_api.v3 import io, ui
+from comfy.cli_args import args
+import folder_paths
+import node_helpers
+
+
+class SaveImage(io.ComfyNodeV3):
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return io.SchemaV3(
+            node_id="SaveImage",
+            display_name="Save Image",
+            description="Saves the input images to your ComfyUI output directory.",
+            category="image",
+            inputs=[
+                io.Image.Input(
+                    "images",
+                    display_name="images",
+                    tooltip="The images to save.",
+                ),
+                io.String.Input(
+                    "filename_prefix",
+                    default="ComfyUI",
+                    tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
+                ),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, images, filename_prefix="ComfyUI"):
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            "", folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
+        )
+        results = []
+        for (batch_number, image) in enumerate(images):
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            if not args.disable_metadata:
+                metadata = PngInfo()
+                if cls.hidden.prompt is not None:
+                    metadata.add_text("prompt", json.dumps(cls.hidden.prompt))
+                if cls.hidden.extra_pnginfo is not None:
+                    for x in cls.hidden.extra_pnginfo:
+                        metadata.add_text(x, json.dumps(cls.hidden.extra_pnginfo[x]))
+
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": "output",
+            })
+            counter += 1
+
+        return io.NodeOutput(ui={"images": results})
+
+
+class PreviewImage(io.ComfyNodeV3):
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return io.SchemaV3(
+            node_id="PreviewImage",
+            display_name="Preview Image",
+            description="Preview the input images.",
+            category="image",
+            inputs=[
+                io.Image.Input(
+                    "images",
+                    display_name="images",
+                    tooltip="The images to preview.",
+                ),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, images):
+        return io.NodeOutput(ui=ui.PreviewImage(images))
+
+
+class LoadImage(io.ComfyNodeV3):
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return io.SchemaV3(
+            node_id="LoadImage",
+            display_name="Load Image",
+            category="image",
+            inputs=[
+                io.Combo.Input(
+                    "image",
+                    display_name="image",
+                    image_upload=True,
+                    image_folder=io.FolderType.input,
+                    content_types=["image"],
+                ),
+            ],
+            outputs=[
+                io.Image.Output(
+                    "IMAGE",
+                ),
+                io.Mask.Output(
+                    "MASK",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
+        img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
+
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            elif i.mode == 'P' and 'transparency' in i.info:
+                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return io.NodeOutput(output_image, output_mask)
+
+    @classmethod
+    def IS_CHANGED(s, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+        return True
+
+
+class LoadImageOutput(io.ComfyNodeV3):
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return io.SchemaV3(
+            node_id="LoadImageOutput",
+            display_name="Load Image (from Outputs)",
+            description="Load an image from the output folder. "
+                        "When the refresh button is clicked, the node will update the image list "
+                        "and automatically select the first image, allowing for easy iteration.",
+            category="image",
+            inputs=[
+                io.Combo.Input(
+                    "image",
+                    display_name="image",
+                    image_upload=True,
+                    image_folder=io.FolderType.output,
+                    content_types=["image"],
+                    remote=io.RemoteOptions(
+                        route="/internal/files/output",
+                        refresh_button=True,
+                        control_after_refresh="first",
+                    ),
+                ),
+            ],
+            outputs=[
+                io.Image.Output(
+                    "IMAGE",
+                ),
+                io.Mask.Output(
+                    "MASK",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
+        img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
+
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            elif i.mode == 'P' and 'transparency' in i.info:
+                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return io.NodeOutput(output_image, output_mask)
+
+    @classmethod
+    def IS_CHANGED(s, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+        return True
+
+
+
+NODES_LIST: list[type[io.ComfyNodeV3]] = [
+    SaveImage,
+    PreviewImage,
+    LoadImage,
+    LoadImageOutput,
+]

comfy_extras/v3/nodes_mask.py

@@ -0,0 +1,32 @@
+from comfy_api.v3 import io, ui
+
+
+class MaskPreview(io.ComfyNodeV3):
+    """Mask Preview - original implement in ComfyUI_essentials.
+
+    https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
+    Upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
+    """
+
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return io.SchemaV3(
+            node_id="MaskPreview",
+            display_name="Convert Mask to Image",
+            category="mask",
+            inputs=[
+                io.Mask.Input(
+                    "masks",
+                    display_name="masks",
+                ),
+            ],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
+
+    @classmethod
+    def execute(cls, masks):
+        return io.NodeOutput(ui=ui.PreviewMask(masks))
+
+
+NODES_LIST: list[type[io.ComfyNodeV3]] = [MaskPreview]

comfy_extras/v3/nodes_webcam.py

@@ -0,0 +1,118 @@
+import hashlib
+import torch
+
+import numpy as np
+from PIL import Image, ImageOps, ImageSequence
+
+from comfy_api.v3 import io
+import nodes
+import folder_paths
+import node_helpers
+
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+
+class WebcamCapture(io.ComfyNodeV3):
+    @classmethod
+    def DEFINE_SCHEMA(cls):
+        return io.SchemaV3(
+            node_id="WebcamCapture",
+            display_name="Webcam Capture",
+            category="image",
+            inputs=[
+                io.Webcam.Input(
+                    "image",
+                    display_name="image",
+                ),
+                io.Int.Input(
+                    "width",
+                    display_name="width",
+                    default=0,
+                    min=0,
+                    max=MAX_RESOLUTION,
+                    step=1,
+                ),
+                io.Int.Input(
+                    "height",
+                    display_name="height",
+                    default=0,
+                    min=0,
+                    max=MAX_RESOLUTION,
+                    step=1,
+                ),
+                io.Boolean.Input(
+                    "capture_on_queue",
+                    default=True,
+                ),
+            ],
+            outputs=[
+                io.Image.Output(
+                    "IMAGE",
+                ),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, image, **kwargs) -> io.NodeOutput:
+        img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
+
+        output_images = []
+        output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+
+        for i in ImageSequence.Iterator(img):
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
+            if i.mode == 'I':
+                i = i.point(lambda i: i * (1 / 255))
+            image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+
+            if image.size[0] != w or image.size[1] != h:
+                continue
+
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            if 'A' in i.getbands():
+                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            elif i.mode == 'P' and 'transparency' in i.info:
+                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
+                mask = 1. - torch.from_numpy(mask)
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
+            output_images.append(image)
+            output_masks.append(mask.unsqueeze(0))
+
+        if len(output_images) > 1 and img.format not in excluded_formats:
+            output_image = torch.cat(output_images, dim=0)
+            output_mask = torch.cat(output_masks, dim=0)
+        else:
+            output_image = output_images[0]
+            output_mask = output_masks[0]
+
+        return io.NodeOutput(output_image, output_mask)
+
+    @classmethod
+    def IS_CHANGED(s, image, width, height, capture_on_queue):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+        return True
+
+
+NODES_LIST: list[type[io.ComfyNodeV3]] = [WebcamCapture]

execution.py

@@ -321,7 +321,10 @@ def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb
         elif isinstance(r, NodeOutput):
             # V3
             if r.ui is not None:
-                uis.append(r.ui.as_dict())
+                if isinstance(r.ui, dict):
+                    uis.append(r.ui)
+                else:
+                    uis.append(r.ui.as_dict())
             if r.expand is not None:
                 has_subgraph = True
                 new_graph = r.expand

nodes.py

@@ -8,11 +8,9 @@ import hashlib
 import traceback
 import math
 import time
-import random
 import logging
 
-from PIL import Image, ImageOps, ImageSequence
-from PIL.PngImagePlugin import PngInfo
+from PIL import Image, ImageOps
 
 import numpy as np
 import safetensors.torch
@@ -1551,181 +1549,6 @@ class KSamplerAdvanced:
         return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)
 
 
-class SaveImage(io.ComfyNodeV3):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="SaveImage",
-            display_name="Save Image",
-            description="Saves the input images to your ComfyUI output directory.",
-            category="image",
-            inputs=[
-                io.Image.Input(
-                    "images",
-                    display_name="images",
-                    tooltip="The images to save.",
-                ),
-                io.String.Input(
-                    "filename_prefix",
-                    default="ComfyUI",
-                    tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes.",
-                ),
-            ],
-            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
-            is_output_node=True,
-        )
-
-    def __init__(self):
-        super().__init__()
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
-        self.compress_level = 4
-
-    def execute(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
-        filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
-        results = list()
-        for (batch_number, image) in enumerate(images):
-            i = 255. * image.cpu().numpy()
-            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
-            metadata = None
-            if not args.disable_metadata:
-                metadata = PngInfo()
-                if prompt is not None:
-                    metadata.add_text("prompt", json.dumps(prompt))
-                if extra_pnginfo is not None:
-                    for x in extra_pnginfo:
-                        metadata.add_text(x, json.dumps(extra_pnginfo[x]))
-
-            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
-            file = f"{filename_with_batch_num}_{counter:05}_.png"
-            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
-            results.append({
-                "filename": file,
-                "subfolder": subfolder,
-                "type": self.type,
-            })
-            counter += 1
-
-        return { "ui": { "images": results } }
-
-
-class PreviewImage(SaveImage):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="PreviewImage",
-            display_name="Preview Image",
-            description="Preview the input images.",
-            category="image",
-            inputs=[
-                io.Image.Input(
-                    "images",
-                    display_name="images",
-                    tooltip="The images to preview.",
-                ),
-            ],
-            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
-            is_output_node=True,
-        )
-
-    def __init__(self):
-        super().__init__()
-        self.output_dir = folder_paths.get_temp_directory()
-        self.type = "temp"
-        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
-        self.compress_level = 1
-
-
-class LoadImage(io.ComfyNodeV3):
-    @classmethod
-    def DEFINE_SCHEMA(cls):
-        return io.SchemaV3(
-            node_id="LoadImage",
-            display_name="Load Image",
-            category="image",
-            inputs=[
-                io.Combo.Input(
-                    "image",
-                    display_name="image",
-                    image_upload=True,
-                    image_folder=io.FolderType.input,
-                    content_types=["image"],
-                ),
-            ],
-            outputs=[
-                io.Image.Output(
-                    "IMAGE",
-                ),
-                io.Mask.Output(
-                    "MASK",
-                ),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, image) -> io.NodeOutput:
-        img = node_helpers.pillow(Image.open, folder_paths.get_annotated_filepath(image))
-
-        output_images = []
-        output_masks = []
-        w, h = None, None
-
-        excluded_formats = ['MPO']
-
-        for i in ImageSequence.Iterator(img):
-            i = node_helpers.pillow(ImageOps.exif_transpose, i)
-
-            if i.mode == 'I':
-                i = i.point(lambda i: i * (1 / 255))
-            image = i.convert("RGB")
-
-            if len(output_images) == 0:
-                w = image.size[0]
-                h = image.size[1]
-
-            if image.size[0] != w or image.size[1] != h:
-                continue
-
-            image = np.array(image).astype(np.float32) / 255.0
-            image = torch.from_numpy(image)[None,]
-            if 'A' in i.getbands():
-                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            elif i.mode == 'P' and 'transparency' in i.info:
-                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
-                mask = 1. - torch.from_numpy(mask)
-            else:
-                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
-            output_images.append(image)
-            output_masks.append(mask.unsqueeze(0))
-
-        if len(output_images) > 1 and img.format not in excluded_formats:
-            output_image = torch.cat(output_images, dim=0)
-            output_mask = torch.cat(output_masks, dim=0)
-        else:
-            output_image = output_images[0]
-            output_mask = output_masks[0]
-
-        return io.NodeOutput(output_image, output_mask)
-
-    @classmethod
-    def IS_CHANGED(s, image):
-        image_path = folder_paths.get_annotated_filepath(image)
-        m = hashlib.sha256()
-        with open(image_path, 'rb') as f:
-            m.update(f.read())
-        return m.digest().hex()
-
-    @classmethod
-    def VALIDATE_INPUTS(s, image):
-        if not folder_paths.exists_annotated_filepath(image):
-            return "Invalid image file: {}".format(image)
-
-        return True
-
-
 class LoadImageMask:
     _color_channels = ["alpha", "red", "green", "blue"]
     @classmethod
@@ -1776,28 +1599,6 @@ class LoadImageMask:
         return True
 
 
-class LoadImageOutput(LoadImage):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("COMBO", {
-                    "image_upload": True,
-                    "image_folder": "output",
-                    "remote": {
-                        "route": "/internal/files/output",
-                        "refresh_button": True,
-                        "control_after_refresh": "first",
-                    },
-                }),
-            }
-        }
-
-    DESCRIPTION = "Load an image from the output folder. When the refresh button is clicked, the node will update the image list and automatically select the first image, allowing for easy iteration."
-    EXPERIMENTAL = True
-    FUNCTION = "load_image"
-
-
 class ImageScale:
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
@@ -1980,11 +1781,7 @@ NODE_CLASS_MAPPINGS = {
     "LatentUpscaleBy": LatentUpscaleBy,
     "LatentFromBatch": LatentFromBatch,
     "RepeatLatentBatch": RepeatLatentBatch,
-    "SaveImage": SaveImage,
-    "PreviewImage": PreviewImage,
-    "LoadImage": LoadImage,
     "LoadImageMask": LoadImageMask,
-    "LoadImageOutput": LoadImageOutput,
     "ImageScale": ImageScale,
     "ImageScaleBy": ImageScaleBy,
     "ImageInvert": ImageInvert,
@@ -2081,11 +1878,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "LatentFromBatch" : "Latent From Batch",
     "RepeatLatentBatch": "Repeat Latent Batch",
     # Image
-    "SaveImage": "Save Image",
-    "PreviewImage": "Preview Image",
-    "LoadImage": "Load Image",
     "LoadImageMask": "Load Image (as Mask)",
-    "LoadImageOutput": "Load Image (from Outputs)",
     "ImageScale": "Upscale Image",
     "ImageScaleBy": "Upscale Image By",
     "ImageUpscaleWithModel": "Upscale Image (using Model)",
@@ -2295,7 +2088,6 @@ def init_builtin_extra_nodes():
         "nodes_align_your_steps.py",
         "nodes_attention_multiply.py",
         "nodes_advanced_samplers.py",
-        "nodes_webcam.py",
         "nodes_audio.py",
         "nodes_sd3.py",
         "nodes_gits.py",
@@ -2330,6 +2122,9 @@ def init_builtin_extra_nodes():
         "nodes_tcfg.py"
         "nodes_v3_test.py",
         "nodes_v1_test.py",
+        "v3/nodes_images.py",
+        "v3/nodes_mask.py",
+        "v3/nodes_webcam.py",
     ]
 
     import_failed = []