94 lines
2.5 KiB
Python
94 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 BatchLoadImageList:
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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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"paths": ("STRING_LIST",),
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},
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"optional": {
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"start": ("INT", {"default": 0, "min": 0}),
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"end": ("INT", {"default": -1, "min": -1}),
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"step": ("INT", {"default": 1, "min": 1}),
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},
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}
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RETURN_TYPES = ("IMAGE", "INT")
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FUNCTION = "execute"
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DESCRIPTION = """
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Loads images from a provided list of paths.
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- Best used with `Batch Save Image Sequence` output.
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- Supports slicing (start, end, step) for partial loading.
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- Optimized with async loading and direct tensor conversion.
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"""
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def execute(
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self,
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paths,
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start=0,
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end=-1,
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step=1,
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):
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if not paths:
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raise RuntimeError("STRING_LIST is empty")
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total = len(paths)
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# Auto-detect end
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if end < 0 or end > total:
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end = total
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if start >= end:
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raise ValueError("Invalid frame range")
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selected = paths[start:end:step]
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if not selected:
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raise RuntimeError("No frames selected after slicing")
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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(selected))
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imgs = []
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# Helper to check existence before loading to keep logic intact
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def load_worker(path):
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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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return load_image(path)
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with ThreadPoolExecutor() as executor:
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for img in executor.map(load_worker, selected):
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imgs.append(img)
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pbar.update(1)
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images = torch.cat(imgs, dim=0)
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return (images, len(selected))
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NODE_CLASS_MAPPINGS = {
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"BatchLoadImageList": BatchLoadImageList
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"BatchLoadImageList": "Batch Load Image List (Advanced)"
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}
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