85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import os
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import torch
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from PIL import Image
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import numpy as np
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def load_image(path: str) -> torch.Tensor:
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"""
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Load image from disk and convert to ComfyUI IMAGE tensor
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Shape: [1, H, W, 3], float32 0..1
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"""
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img = Image.open(path).convert("RGB")
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# Optimize: Direct to tensor -> float conversion (avoids intermediate float64 numpy array)
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return torch.from_numpy(np.array(img)).float().div_(255.0)[None,]
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class BatchLoadImageSequence:
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CATEGORY = "FrameIO"
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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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"path_pattern": (
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"STRING",
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{"default": "./frame{:06d}.webp", "multiline": False},
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),
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"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
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"frame_count": ("INT", {"default": 16, "min": 1, "step": 1}),
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"ignore_missing_images": (("false", "true"), {"default": "false"}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "execute"
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def execute(
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self,
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path_pattern: str,
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start_index: int,
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frame_count: int,
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ignore_missing_images: str,
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):
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ignore_missing_images = ignore_missing_images == "true"
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image_paths = []
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for i in range(start_index, start_index + frame_count):
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try:
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image_paths.append(path_pattern.format(i))
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except KeyError:
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image_paths.append(path_pattern.format(i=i))
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if ignore_missing_images:
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image_paths = [p for p in image_paths if os.path.exists(p)]
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else:
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for path in image_paths:
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if not os.path.exists(path):
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raise FileNotFoundError(f"Image does not exist: {path}")
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if len(image_paths) == 0:
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raise RuntimeError("Image sequence empty - no images to load")
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from concurrent.futures import ThreadPoolExecutor
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from comfy.utils import ProgressBar
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pbar = ProgressBar(len(image_paths))
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imgs = []
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with ThreadPoolExecutor() as executor:
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for img in executor.map(load_image, image_paths):
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imgs.append(img)
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pbar.update(1)
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return (torch.cat(imgs, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"BatchLoadImageSequence": BatchLoadImageSequence
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"BatchLoadImageSequence": "Batch Load Image Sequence"
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}
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