349 lines
13 KiB
Python
349 lines
13 KiB
Python
"""
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ComfyUI Toggle Pass - Toggle nodes + Image IO + Multi-Ref Encode
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Author: Clever
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"""
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from .multi_ref_encode import NODE_CLASS_MAPPINGS as MULTI_REF_NODES, NODE_DISPLAY_NAME_MAPPINGS as MULTI_REF_NAMES
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from .unified_loader import NODE_CLASS_MAPPINGS as UNIFIED_NODES, NODE_DISPLAY_NAME_MAPPINGS as UNIFIED_NAMES
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import os
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import json
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import random
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import hashlib
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import torch
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import numpy as np
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from PIL import Image, ImageOps, ImageSequence, ImageFile
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from PIL.PngImagePlugin import PngInfo
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import comfy.model_management
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import comfy.utils
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import folder_paths
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import node_helpers
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WEB_DIRECTORY = "./js"
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_last_saved_path = {}
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# ==================== Toggle Load Image ====================
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class ToggleLoadImage:
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"""Load image from input folder with privacy toggle support."""
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@classmethod
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def INPUT_TYPES(cls):
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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 {
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"required": {
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"image": (sorted(files), {"image_upload": True}),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK")
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RETURN_NAMES = ("IMAGE", "MASK")
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FUNCTION = "load_image"
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CATEGORY = "Toggle-Pass"
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DESCRIPTION = "Load image with privacy toggle."
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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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# 尝试视频帧提取（如果安装了 comfy-extras 的 InputImpl）
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try:
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from comfy_extras.nodes_input import InputImpl
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components = InputImpl.VideoFromFile(image_path).get_components()
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if components.images.shape[0] > 0:
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return (components.images, 1.0 - components.alpha[..., -1] if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=torch.float32, device="cpu"))
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except Exception:
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pass
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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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dtype = comfy.model_management.intermediate_dtype()
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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.to(dtype=dtype))
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output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
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if img.format == "MPO":
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break
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if len(output_images) > 1:
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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(cls, 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(cls, 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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# ==================== Toggle Preview Image ====================
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class TogglePreviewImage:
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"""Preview image with privacy toggle support. Saves to temp like built-in PreviewImage."""
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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.compress_level = 1
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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},
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"hidden": {
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"prompt": "PROMPT",
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"extra_pnginfo": "EXTRA_PNGINFO",
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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OUTPUT_NODE = True
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FUNCTION = "preview"
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CATEGORY = "Toggle-Pass"
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DESCRIPTION = "Preview image with privacy toggle. Saves to temp like PreviewImage."
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def preview(self, images, prompt=None, extra_pnginfo=None):
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results = []
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for idx, img in enumerate(images):
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img_np = 255. * img.cpu().numpy()
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pil_img = Image.fromarray(np.clip(img_np, 0, 255).astype(np.uint8))
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filename = f"toggle_preview_{idx:05}.png"
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filepath = os.path.join(self.output_dir, filename)
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pil_img.save(filepath, format="PNG", compress_level=self.compress_level)
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results.append({
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"filename": filename,
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"subfolder": "",
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"type": self.type,
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})
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return {"result": (images,), "ui": {"images": results}}
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class SaveLoadImage:
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"""Save image AND output it. Toggle OFF to auto-load last saved image instead."""
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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(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"use_saved": (["Save", "Load"],),
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},
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"optional": {
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"filename_prefix": ("STRING", {"default": "ComfyUI"}),
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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 = ("IMAGE", "MASK")
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RETURN_NAMES = ("IMAGE", "MASK")
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OUTPUT_NODE = True
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FUNCTION = "execute"
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CATEGORY = "Toggle-Pass"
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DESCRIPTION = "Save image and output IMAGE. Toggle use_saved=ON to reuse last saved image."
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def execute(self, images, use_saved="Save", filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
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if use_saved == "Load":
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return self._load_last()
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else:
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return self._save(images, filename_prefix, prompt, extra_pnginfo)
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def _save(self, images, filename_prefix, prompt, extra_pnginfo):
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if images is None:
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images = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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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(
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filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
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)
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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 comfy.utils.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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filepath = os.path.join(full_output_folder, file)
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img.save(filepath, 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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if id(self) not in _last_saved_path:
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_last_saved_path[id(self)] = filepath
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counter += 1
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last_file = os.path.join(full_output_folder, results[0]["filename"])
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_last_saved_path[id(self)] = last_file
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mask = torch.zeros((images.shape[0], images.shape[1], images.shape[2]), dtype=torch.float32)
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return {"result": (images, mask), "ui": {"images": results}}
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def _load_last(self):
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node_id = id(self)
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if node_id in _last_saved_path and os.path.isfile(_last_saved_path[node_id]):
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filepath = _last_saved_path[node_id]
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img = node_helpers.pillow(Image.open, filepath)
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output_images = []
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output_masks = []
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w, h = None, None
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dtype = comfy.model_management.intermediate_dtype()
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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 x: x * (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((h, w), dtype=torch.float32, device="cpu")
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output_images.append(image.to(dtype=dtype))
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output_masks.append(mask.unsqueeze(0).to(dtype=dtype))
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if img.format == "MPO":
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break
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if len(output_images) > 1:
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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 {"result": (output_image, output_mask), "ui": {"images": []}}
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dummy = torch.zeros((1, 64, 64, 3), dtype=torch.float32)
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dummy_mask = torch.zeros((1, 64, 64), dtype=torch.float32)
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return {"result": (dummy, dummy_mask), "ui": {"images": []}}
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class ToggleEmptyLatent:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"width": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}),
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"height": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
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"enabled": (["ON", "OFF"],),
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},
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"optional": {
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"fallback_width": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}),
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"fallback_height": ("INT", {"default": 1024, "min": 16, "max": 16384, "step": 8}),
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},
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}
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RETURN_TYPES = ("LATENT",)
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RETURN_NAMES = ("LATENT",)
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FUNCTION = "generate"
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CATEGORY = "Toggle-Pass"
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def generate(self, width, height, batch_size=1, enabled="ON", fallback_width=None, fallback_height=None):
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if fallback_width is None:
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fallback_width = 1024
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if fallback_height is None:
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fallback_height = 1024
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if enabled != "ON":
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width = fallback_width
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height = fallback_height
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latent = torch.zeros([batch_size, 4, height // 8, width // 8],
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device=comfy.model_management.intermediate_device(),
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dtype=comfy.model_management.intermediate_dtype())
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return ({"samples": latent, "downscale_ratio_spacial": 8},)
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NODE_CLASS_MAPPINGS = {
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**MULTI_REF_NODES,
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**UNIFIED_NODES,
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"ToggleEmptyLatent": ToggleEmptyLatent,
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"SaveLoadImage": SaveLoadImage,
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"ToggleLoadImage": ToggleLoadImage,
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"TogglePreviewImage": TogglePreviewImage,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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**MULTI_REF_NAMES,
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**UNIFIED_NAMES,
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"ToggleEmptyLatent": "Toggle Empty Latent",
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"SaveLoadImage": "Save/Load Image",
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"ToggleLoadImage": "Toggle Load Image",
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"TogglePreviewImage": "Toggle Preview Image",
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}
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