put V1 nodes back

comfy_extras/nodes_mask.py

@@ -3,7 +3,10 @@ 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):
@@ -362,6 +365,30 @@ 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,
@@ -376,8 +403,10 @@ 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

@@ -0,0 +1,37 @@
+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

@@ -13,12 +13,12 @@ import folder_paths
 import node_helpers
 
 
-class SaveImage(io.ComfyNodeV3):
+class SaveImage_V3(io.ComfyNodeV3):
     @classmethod
     def DEFINE_SCHEMA(cls):
         return io.SchemaV3(
-            node_id="SaveImage",
+            node_id="SaveImage_V3",
-            display_name="Save Image",
+            display_name="Save Image _V3",
             description="Saves the input images to your ComfyUI output directory.",
             category="image",
             inputs=[
@@ -68,12 +68,12 @@ class SaveImage(io.ComfyNodeV3):
         return io.NodeOutput(ui={"images": results})
 
 
-class PreviewImage(io.ComfyNodeV3):
+class PreviewImage_V3(io.ComfyNodeV3):
     @classmethod
     def DEFINE_SCHEMA(cls):
         return io.SchemaV3(
-            node_id="PreviewImage",
+            node_id="PreviewImage_V3",
-            display_name="Preview Image",
+            display_name="Preview Image _V3",
             description="Preview the input images.",
             category="image",
             inputs=[
@@ -92,12 +92,12 @@ class PreviewImage(io.ComfyNodeV3):
         return io.NodeOutput(ui=ui.PreviewImage(images))
 
 
-class LoadImage(io.ComfyNodeV3):
+class LoadImage_V3(io.ComfyNodeV3):
     @classmethod
     def DEFINE_SCHEMA(cls):
         return io.SchemaV3(
-            node_id="LoadImage",
+            node_id="LoadImage_V3",
-            display_name="Load Image",
+            display_name="Load Image _V3",
             category="image",
             inputs=[
                 io.Combo.Input(
@@ -186,12 +186,12 @@ class LoadImage(io.ComfyNodeV3):
         return True
 
 
-class LoadImageOutput(io.ComfyNodeV3):
+class LoadImageOutput_V3(io.ComfyNodeV3):
     @classmethod
     def DEFINE_SCHEMA(cls):
         return io.SchemaV3(
-            node_id="LoadImageOutput",
+            node_id="LoadImageOutput_V3",
-            display_name="Load Image (from Outputs)",
+            display_name="Load Image (from Outputs) _V3",
             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.",
@@ -283,8 +283,8 @@ class LoadImageOutput(io.ComfyNodeV3):
 
 
 NODES_LIST: list[type[io.ComfyNodeV3]] = [
-    SaveImage,
+    SaveImage_V3,
-    PreviewImage,
+    PreviewImage_V3,
-    LoadImage,
+    LoadImage_V3,
-    LoadImageOutput,
+    LoadImageOutput_V3,
 ]

comfy_extras/v3/nodes_mask.py

@@ -1,7 +1,7 @@
 from comfy_api.v3 import io, ui
 
 
-class MaskPreview(io.ComfyNodeV3):
+class MaskPreview_V3(io.ComfyNodeV3):
     """Mask Preview - original implement in ComfyUI_essentials.
 
     https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
@@ -11,8 +11,8 @@ class MaskPreview(io.ComfyNodeV3):
     @classmethod
     def DEFINE_SCHEMA(cls):
         return io.SchemaV3(
-            node_id="MaskPreview",
+            node_id="MaskPreview_V3",
-            display_name="Convert Mask to Image",
+            display_name="Convert Mask to Image _V3",
             category="mask",
             inputs=[
                 io.Mask.Input(
@@ -29,4 +29,4 @@ class MaskPreview(io.ComfyNodeV3):
         return io.NodeOutput(ui=ui.PreviewMask(masks))
 
 
-NODES_LIST: list[type[io.ComfyNodeV3]] = [MaskPreview]
+NODES_LIST: list[type[io.ComfyNodeV3]] = [MaskPreview_V3]

comfy_extras/v3/nodes_webcam.py

@@ -13,12 +13,12 @@ import node_helpers
 MAX_RESOLUTION = nodes.MAX_RESOLUTION
 
 
-class WebcamCapture(io.ComfyNodeV3):
+class WebcamCapture_V3(io.ComfyNodeV3):
     @classmethod
     def DEFINE_SCHEMA(cls):
         return io.SchemaV3(
-            node_id="WebcamCapture",
+            node_id="WebcamCapture_V3",
-            display_name="Webcam Capture",
+            display_name="Webcam Capture _V3",
             category="image",
             inputs=[
                 io.Webcam.Input(
@@ -114,4 +114,4 @@ class WebcamCapture(io.ComfyNodeV3):
         return True
 
 
-NODES_LIST: list[type[io.ComfyNodeV3]] = [WebcamCapture]
+NODES_LIST: list[type[io.ComfyNodeV3]] = [WebcamCapture_V3]

nodes.py

@@ -8,9 +8,11 @@ import hashlib
 import traceback
 import math
 import time
+import random
 import logging
 
-from PIL import Image, ImageOps
+from PIL import Image, ImageOps, ImageSequence
+from PIL.PngImagePlugin import PngInfo
 
 import numpy as np
 import safetensors.torch
@@ -1548,6 +1550,150 @@ class KSamplerAdvanced:
             disable_noise = True
         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:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "images": ("IMAGE", {"tooltip": "The images to save."}),
+                "filename_prefix": ("STRING", {"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": {
+                "prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
+            },
+        }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image"
+    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
+
+    def save_images(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):
+    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 = 1
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"images": ("IMAGE", ), },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+class LoadImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        files = folder_paths.filter_files_content_types(files, ["image"])
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True})},
+                }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("IMAGE", "MASK")
+    FUNCTION = "load_image"
+    def load_image(self, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+
+        img = node_helpers.pillow(Image.open, image_path)
+
+        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 (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"]
@@ -1599,6 +1745,28 @@ 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"]
@@ -1781,7 +1949,11 @@ 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,
@@ -1878,7 +2050,11 @@ 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)",
@@ -2088,6 +2264,7 @@ 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",