solidlime
21bb26d72f
File picker previously embedded base64 image data in the image_data widget, bloating workflow JSON past the 5MiB localStorage limit (Failed to save workflow draft). Now uploads via POST /upload/image with type=temp and stores a small '<path> [temp]' reference; backend resolves it with get_annotated_filepath. Base64 data URLs from older saves still load via backward-compat path.
231 lines
8.6 KiB
Python
231 lines
8.6 KiB
Python
"""
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Random Image Picker Node Implementation
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"""
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import os
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import random
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from pathlib import Path
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from typing import Tuple, List
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import folder_paths
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from PIL import Image
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import numpy as np
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import torch
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from nodes import SaveImage
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class RandomImagePicker(SaveImage):
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"""
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ComfyUI node that loads either a single image or a random image from a folder.
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Supports subfolder scanning and outputs image with resolution information.
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"""
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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(cls):
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# Get default input directory from ComfyUI
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try:
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default_path = folder_paths.get_input_directory()
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except:
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default_path = ""
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return {
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"required": {
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"folder_path": ("STRING", {
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"default": default_path,
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"multiline": False,
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"dynamicPrompts": False,
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"tooltip": "Folder path for random image selection (Folder mode only)"
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}),
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"folder_mode": ("BOOLEAN", {
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"default": False,
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"label_on": "Folder",
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"label_off": "Single",
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"tooltip": "Single: Use file picker | Folder: Random from folder path"
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}),
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"include_subfolders": ("BOOLEAN", {
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"default": False,
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"label_on": "ON",
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"label_off": "OFF",
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"tooltip": "Include subfolders when scanning in Folder mode"
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 0xffffffffffffffff,
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"tooltip": "Random seed for reproducible selection in Folder mode"
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}),
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"image_data": ("STRING", {
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"default": "",
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"multiline": True,
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"tooltip": "Image reference (temp) from file picker (auto-filled)"
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}),
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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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FUNCTION = "load_image"
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CATEGORY = "image"
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OUTPUT_NODE = True
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@staticmethod
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def get_image_files(directory: str, include_subfolders: bool = False) -> List[str]:
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"""
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Get all image files from a directory.
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Args:
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directory: Directory path to scan
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include_subfolders: Whether to include subfolders in the scan
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Returns:
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List of image file paths
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"""
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supported_formats = {'.png', '.jpg', '.jpeg', '.webp', '.bmp', '.gif'}
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image_files = []
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if include_subfolders:
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for root, _, files in os.walk(directory):
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for file in files:
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if Path(file).suffix.lower() in supported_formats:
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image_files.append(os.path.join(root, file))
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else:
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for file in os.listdir(directory):
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file_path = os.path.join(directory, file)
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if os.path.isfile(file_path) and Path(file).suffix.lower() in supported_formats:
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image_files.append(file_path)
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return image_files
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@staticmethod
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def load_image_file(image_path: str) -> Tuple[torch.Tensor, int, int]:
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"""
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Load an image file and convert to ComfyUI format.
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Args:
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image_path: Path to the image file
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Returns:
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Tuple of (image_tensor, width, height)
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"""
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img = Image.open(image_path)
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# Convert to RGB if needed
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if img.mode != 'RGB':
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img = img.convert('RGB')
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# Get dimensions
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width, height = img.size
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# Convert to numpy array
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img_array = np.array(img).astype(np.float32) / 255.0
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# Convert to torch tensor and add batch dimension
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# ComfyUI expects [batch, height, width, channels]
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img_tensor = torch.from_numpy(img_array)[None,]
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return img_tensor, width, height
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def load_image(self, folder_path: str, folder_mode: bool, include_subfolders: bool, seed: int, image_data: str = ""):
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"""
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Load image based on mode settings.
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Args:
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folder_path: Folder path for random selection (used when folder_mode=True)
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folder_mode: True=Folder random selection, False=File picker
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include_subfolders: Whether to scan subfolders
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seed: Random seed for folder mode
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image_data: Base64 encoded image data from file picker (used when folder_mode=False)
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Returns:
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Dict with ui (for preview) and result (IMAGE tensor)
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"""
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if folder_mode:
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# Folder mode: random selection from folder_path
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if not folder_path or not os.path.exists(folder_path):
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raise ValueError(f"Folder mode is enabled but folder_path is invalid: {folder_path}")
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if not os.path.isdir(folder_path):
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raise ValueError(f"Folder mode requires a directory path, not a file: {folder_path}")
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image_files = self.get_image_files(folder_path, include_subfolders)
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if not image_files:
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raise ValueError(f"No image files found in directory: {folder_path}")
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# Use seed for reproducible random selection
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random.seed(seed)
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selected_image = random.choice(image_files)
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print(f"[Random Image Picker] Mode: Folder")
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print(f"[Random Image Picker] Folder: {folder_path}")
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print(f"[Random Image Picker] Selected: {selected_image}")
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img_tensor, width, height = self.load_image_file(selected_image)
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print(f"[Random Image Picker] Resolution: {width}x{height}")
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# Save for preview and return with image tensor
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preview = self.save_images(img_tensor, filename_prefix="RandomPicker")
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return {"ui": preview["ui"], "result": (img_tensor,)}
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else:
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# Single file mode: use file picker (image_data)
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if not image_data:
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raise ValueError(
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"Single file mode is enabled but no image selected.\n\n"
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"Please use the 📁 Choose File button to select an image."
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)
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if image_data.startswith("data:"):
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# Backward compatibility: base64 data URL from older versions
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import base64
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import io
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try:
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# Remove data URL prefix if present
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image_data = image_data.split(',', 1)[1]
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# Decode base64
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img_bytes = base64.b64decode(image_data)
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img = Image.open(io.BytesIO(img_bytes))
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# Convert to RGB if needed
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if img.mode != 'RGB':
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img = img.convert('RGB')
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# Get dimensions
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width, height = img.size
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# Convert to numpy array
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img_array = np.array(img).astype(np.float32) / 255.0
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# Convert to torch tensor and add batch dimension
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img_tensor = torch.from_numpy(img_array)[None,]
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except Exception as e:
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raise ValueError(f"Failed to load image from file picker: {str(e)}")
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else:
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# New path: resolve temp image reference (e.g. "name.png [temp]")
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image_path = folder_paths.get_annotated_filepath(image_data)
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img_tensor, width, height = self.load_image_file(image_path)
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print(f"[Random Image Picker] Mode: Single (File Picker)")
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print(f"[Random Image Picker] Resolution: {width}x{height}")
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# Save for preview and return with image tensor
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preview = self.save_images(img_tensor, filename_prefix="RandomPicker")
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return {"ui": preview["ui"], "result": (img_tensor,)}
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# Node registration
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NODE_CLASS_MAPPINGS = {
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"RandomImagePicker": RandomImagePicker
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"RandomImagePicker": "Random Image Picker"
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}
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