mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-07-27 16:26:39 +00:00
put V1 nodes back
This commit is contained in:
bigcat88
parent
965d2f9b8f
commit
d8b91bb84e
@ -3,7 +3,10 @@ import scipy.ndimage
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import torch
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import comfy.utils
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import node_helpers
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import folder_paths
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import random
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import nodes
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from nodes import MAX_RESOLUTION
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def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
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@ -362,6 +365,30 @@ class ThresholdMask:
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mask = (mask > value).float()
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return (mask,)
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# Mask Preview - original implement from
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# https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
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# upstream requested in https://github.com/Kosinkadink/rfcs/blob/main/rfcs/0000-corenodes.md#preview-nodes
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class MaskPreview(nodes.SaveImage):
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def __init__(self):
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
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self.compress_level = 4
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {"mask": ("MASK",), },
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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FUNCTION = "execute"
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CATEGORY = "mask"
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def execute(self, mask, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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preview = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
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return self.save_images(preview, filename_prefix, prompt, extra_pnginfo)
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NODE_CLASS_MAPPINGS = {
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"LatentCompositeMasked": LatentCompositeMasked,
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@ -376,8 +403,10 @@ NODE_CLASS_MAPPINGS = {
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"FeatherMask": FeatherMask,
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"GrowMask": GrowMask,
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"ThresholdMask": ThresholdMask,
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"MaskPreview": MaskPreview
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ImageToMask": "Convert Image to Mask",
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"MaskToImage": "Convert Mask to Image",
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}
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37
comfy_extras/nodes_webcam.py
Normal file
37
comfy_extras/nodes_webcam.py
Normal file
@ -0,0 +1,37 @@
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import nodes
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import folder_paths
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MAX_RESOLUTION = nodes.MAX_RESOLUTION
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class WebcamCapture(nodes.LoadImage):
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("WEBCAM", {}),
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"width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
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"height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
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"capture_on_queue": ("BOOLEAN", {"default": True}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "load_capture"
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CATEGORY = "image"
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def load_capture(self, image, **kwargs):
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return super().load_image(folder_paths.get_annotated_filepath(image))
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@classmethod
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def IS_CHANGED(cls, image, width, height, capture_on_queue):
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return super().IS_CHANGED(image)
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NODE_CLASS_MAPPINGS = {
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"WebcamCapture": WebcamCapture,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"WebcamCapture": "Webcam Capture",
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}
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@ -13,12 +13,12 @@ import folder_paths
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import node_helpers
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class SaveImage(io.ComfyNodeV3):
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class SaveImage_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="SaveImage",
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display_name="Save Image",
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node_id="SaveImage_V3",
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display_name="Save Image _V3",
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description="Saves the input images to your ComfyUI output directory.",
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category="image",
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inputs=[
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@ -68,12 +68,12 @@ class SaveImage(io.ComfyNodeV3):
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return io.NodeOutput(ui={"images": results})
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class PreviewImage(io.ComfyNodeV3):
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class PreviewImage_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="PreviewImage",
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display_name="Preview Image",
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node_id="PreviewImage_V3",
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display_name="Preview Image _V3",
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description="Preview the input images.",
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category="image",
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inputs=[
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@ -92,12 +92,12 @@ class PreviewImage(io.ComfyNodeV3):
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return io.NodeOutput(ui=ui.PreviewImage(images))
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class LoadImage(io.ComfyNodeV3):
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class LoadImage_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="LoadImage",
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display_name="Load Image",
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node_id="LoadImage_V3",
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display_name="Load Image _V3",
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category="image",
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inputs=[
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io.Combo.Input(
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@ -186,12 +186,12 @@ class LoadImage(io.ComfyNodeV3):
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return True
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class LoadImageOutput(io.ComfyNodeV3):
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class LoadImageOutput_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="LoadImageOutput",
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display_name="Load Image (from Outputs)",
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node_id="LoadImageOutput_V3",
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display_name="Load Image (from Outputs) _V3",
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description="Load an image from the output folder. "
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"When the refresh button is clicked, the node will update the image list "
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"and automatically select the first image, allowing for easy iteration.",
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@ -283,8 +283,8 @@ class LoadImageOutput(io.ComfyNodeV3):
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NODES_LIST: list[type[io.ComfyNodeV3]] = [
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SaveImage,
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PreviewImage,
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LoadImage,
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LoadImageOutput,
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SaveImage_V3,
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PreviewImage_V3,
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LoadImage_V3,
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LoadImageOutput_V3,
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]
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@ -1,7 +1,7 @@
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from comfy_api.v3 import io, ui
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class MaskPreview(io.ComfyNodeV3):
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class MaskPreview_V3(io.ComfyNodeV3):
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"""Mask Preview - original implement in ComfyUI_essentials.
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https://github.com/cubiq/ComfyUI_essentials/blob/9d9f4bedfc9f0321c19faf71855e228c93bd0dc9/mask.py#L81
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@ -11,8 +11,8 @@ class MaskPreview(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="MaskPreview",
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display_name="Convert Mask to Image",
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node_id="MaskPreview_V3",
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display_name="Convert Mask to Image _V3",
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category="mask",
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inputs=[
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io.Mask.Input(
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@ -29,4 +29,4 @@ class MaskPreview(io.ComfyNodeV3):
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return io.NodeOutput(ui=ui.PreviewMask(masks))
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NODES_LIST: list[type[io.ComfyNodeV3]] = [MaskPreview]
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NODES_LIST: list[type[io.ComfyNodeV3]] = [MaskPreview_V3]
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@ -13,12 +13,12 @@ import node_helpers
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MAX_RESOLUTION = nodes.MAX_RESOLUTION
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class WebcamCapture(io.ComfyNodeV3):
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class WebcamCapture_V3(io.ComfyNodeV3):
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@classmethod
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def DEFINE_SCHEMA(cls):
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return io.SchemaV3(
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node_id="WebcamCapture",
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display_name="Webcam Capture",
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node_id="WebcamCapture_V3",
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display_name="Webcam Capture _V3",
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category="image",
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inputs=[
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io.Webcam.Input(
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@ -114,4 +114,4 @@ class WebcamCapture(io.ComfyNodeV3):
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return True
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NODES_LIST: list[type[io.ComfyNodeV3]] = [WebcamCapture]
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NODES_LIST: list[type[io.ComfyNodeV3]] = [WebcamCapture_V3]
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179
nodes.py
179
nodes.py
@ -8,9 +8,11 @@ import hashlib
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import traceback
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import math
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import time
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import random
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import logging
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from PIL import Image, ImageOps
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from PIL import Image, ImageOps, ImageSequence
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from PIL.PngImagePlugin import PngInfo
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import numpy as np
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import safetensors.torch
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@ -1548,6 +1550,150 @@ class KSamplerAdvanced:
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disable_noise = True
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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)
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class SaveImage:
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def __init__(self):
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self.output_dir = folder_paths.get_output_directory()
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self.type = "output"
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self.prefix_append = ""
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self.compress_level = 4
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"images": ("IMAGE", {"tooltip": "The images to save."}),
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"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."})
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},
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"hidden": {
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"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"
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},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_images"
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OUTPUT_NODE = True
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CATEGORY = "image"
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DESCRIPTION = "Saves the input images to your ComfyUI output directory."
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def save_images(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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filename_prefix += self.prefix_append
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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])
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results = list()
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for (batch_number, image) in enumerate(images):
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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metadata = None
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if not args.disable_metadata:
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metadata = PngInfo()
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if prompt is not None:
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metadata.add_text("prompt", json.dumps(prompt))
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if extra_pnginfo is not None:
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for x in extra_pnginfo:
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metadata.add_text(x, json.dumps(extra_pnginfo[x]))
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filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
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file = f"{filename_with_batch_num}_{counter:05}_.png"
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img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
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results.append({
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"filename": file,
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"subfolder": subfolder,
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"type": self.type
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})
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counter += 1
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return { "ui": { "images": results } }
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class PreviewImage(SaveImage):
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def __init__(self):
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self.output_dir = folder_paths.get_temp_directory()
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self.type = "temp"
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self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
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self.compress_level = 1
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@classmethod
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def INPUT_TYPES(s):
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return {"required":
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{"images": ("IMAGE", ), },
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"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
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}
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class LoadImage:
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@classmethod
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def INPUT_TYPES(s):
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input_dir = folder_paths.get_input_directory()
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files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
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files = folder_paths.filter_files_content_types(files, ["image"])
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return {"required":
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{"image": (sorted(files), {"image_upload": True})},
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}
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CATEGORY = "image"
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RETURN_TYPES = ("IMAGE", "MASK")
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FUNCTION = "load_image"
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def load_image(self, image):
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image_path = folder_paths.get_annotated_filepath(image)
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img = node_helpers.pillow(Image.open, image_path)
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output_images = []
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output_masks = []
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w, h = None, None
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excluded_formats = ['MPO']
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for i in ImageSequence.Iterator(img):
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i = node_helpers.pillow(ImageOps.exif_transpose, i)
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if i.mode == 'I':
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i = i.point(lambda i: i * (1 / 255))
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image = i.convert("RGB")
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if len(output_images) == 0:
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w = image.size[0]
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h = image.size[1]
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if image.size[0] != w or image.size[1] != h:
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continue
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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if 'A' in i.getbands():
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mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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elif i.mode == 'P' and 'transparency' in i.info:
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mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
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mask = 1. - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
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output_images.append(image)
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output_masks.append(mask.unsqueeze(0))
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if len(output_images) > 1 and img.format not in excluded_formats:
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output_image = torch.cat(output_images, dim=0)
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output_mask = torch.cat(output_masks, dim=0)
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else:
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output_image = output_images[0]
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output_mask = output_masks[0]
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return (output_image, output_mask)
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@classmethod
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def IS_CHANGED(s, image):
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image_path = folder_paths.get_annotated_filepath(image)
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m = hashlib.sha256()
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with open(image_path, 'rb') as f:
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m.update(f.read())
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return m.digest().hex()
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@classmethod
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def VALIDATE_INPUTS(s, image):
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if not folder_paths.exists_annotated_filepath(image):
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return "Invalid image file: {}".format(image)
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return True
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class LoadImageMask:
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_color_channels = ["alpha", "red", "green", "blue"]
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@ -1599,6 +1745,28 @@ class LoadImageMask:
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return True
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class LoadImageOutput(LoadImage):
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("COMBO", {
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"image_upload": True,
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"image_folder": "output",
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"remote": {
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"route": "/internal/files/output",
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"refresh_button": True,
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"control_after_refresh": "first",
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},
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}),
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}
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}
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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."
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EXPERIMENTAL = True
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FUNCTION = "load_image"
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class ImageScale:
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upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
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crop_methods = ["disabled", "center"]
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@ -1781,7 +1949,11 @@ NODE_CLASS_MAPPINGS = {
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"LatentUpscaleBy": LatentUpscaleBy,
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"LatentFromBatch": LatentFromBatch,
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"RepeatLatentBatch": RepeatLatentBatch,
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"SaveImage": SaveImage,
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"PreviewImage": PreviewImage,
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"LoadImage": LoadImage,
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"LoadImageMask": LoadImageMask,
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"LoadImageOutput": LoadImageOutput,
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"ImageScale": ImageScale,
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"ImageScaleBy": ImageScaleBy,
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"ImageInvert": ImageInvert,
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@ -1878,7 +2050,11 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"LatentFromBatch" : "Latent From Batch",
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"RepeatLatentBatch": "Repeat Latent Batch",
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# Image
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"SaveImage": "Save Image",
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"PreviewImage": "Preview Image",
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"LoadImage": "Load Image",
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"LoadImageMask": "Load Image (as Mask)",
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"LoadImageOutput": "Load Image (from Outputs)",
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"ImageScale": "Upscale Image",
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"ImageScaleBy": "Upscale Image By",
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"ImageUpscaleWithModel": "Upscale Image (using Model)",
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@ -2088,6 +2264,7 @@ def init_builtin_extra_nodes():
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"nodes_align_your_steps.py",
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"nodes_attention_multiply.py",
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"nodes_advanced_samplers.py",
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"nodes_webcam.py",
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"nodes_audio.py",
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"nodes_sd3.py",
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"nodes_gits.py",
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