# Documentation - Class name: Stack Images - Category: List Stuff - Output node: True - Repo Ref: https://github.com/comfyanonymous/ComfyUI Stack Images节点被设计用于将多个图像聚合成一个复合图像。它支持多种堆叠模式和配置，包括处理图像批次、应用特定堆叠方向，以及在水平和垂直方向上添加可选标签的能力。这个功能对于以结构化和连贯的方式可视化图像集合特别有用。 # Input types ## Required - images - 要堆叠在一起的图像列表。这个参数对于定义将被处理并组合成单个复合图像的图像集至关重要。 - Comfy dtype: IMAGE - Python dtype: List[Tensor] - splits - 定义如何将图像分割成批次或组进行堆叠。这个参数影响最终复合图像中图像的组织方式。 - Comfy dtype: INT - Python dtype: List[int] - stack_mode - 指定批次内图像堆叠的方向（水平或垂直）。这影响复合图像的布局。 - Comfy dtype: COMBO[STRING] - Python dtype: List[str] - batch_stack_mode - 决定图像批次的堆叠方向。这个参数对于定义复合图像的整体结构至关重要。 - Comfy dtype: COMBO[STRING] - Python dtype: List[str] ## Optional - horizontal_labels - 水平轴的可选标签，在堆叠方向允许水平标记时使用。增强复合图像的可解释性。 - Comfy dtype: * - Python dtype: Optional[List[str]] - vertical_labels - 垂直轴的可选标签，在堆叠方向允许垂直标记时使用。增强复合图像的可解释性。 - Comfy dtype: * - Python dtype: Optional[List[str]] # Output types - Image - 堆叠后产生的复合图像。 - Comfy dtype: IMAGE - Python dtype: Tensor ## Usage tips - Infra type: `GPU` - Common nodes: unknown ## Source code ```python class StackImages: def __init__(self) -> None: pass @classmethod def INPUT_TYPES(s) -> Dict[str, Dict[str, Any]]: return { "required": { "images": ("IMAGE",), "splits": ("INT", {"forceInput": True, "min": 1}), "stack_mode": (["horizontal", "vertical"], {"default": "horizontal"}), "batch_stack_mode": (["horizontal", "vertical"], {"default": "horizontal"}), }, "optional": { "horizontal_labels": (ANY,{}), "vertical_labels": (ANY,{}), } } RELOAD_INST = True RETURN_TYPES = ("IMAGE",) RETURN_NAMES = ("Image",) INPUT_IS_LIST = (True,) OUTPUT_IS_LIST = (False,) OUTPUT_NODE = True FUNCTION = "stack_images" CATEGORY = "List Stuff" def stack_images( self, images: List[Tensor], splits: List[int], stack_mode: List[str], batch_stack_mode: List[str], horizontal_labels: Optional[List[str]] = None, vertical_labels: Optional[List[str]] = None, ) -> Tuple[Tensor]: if len(stack_mode) != 1: raise Exception("Only single stack mode supported.") if len(batch_stack_mode) != 1: raise Exception("Only single batch stack mode supported.") stack_direction = stack_mode[0] batch_stack_direction = batch_stack_mode[0] if len(splits) == 1: splits = splits * (int(len(images) / splits[0])) if sum(splits) != len(images): splits.append(len(images) - sum(splits)) else: if sum(splits) != len(images): raise Exception("Sum of splits must equal number of images.") batches = images batch_size = len(batches[0]) image_h, image_w, _ = batches[0][0].size() if batch_stack_direction == "horizontal": batch_h = image_h # stack horizontally batch_w = image_w * batch_size else: # stack vertically batch_h = image_h * batch_size batch_w = image_w if stack_direction == "horizontal": full_w = batch_w * len(splits) full_h = batch_h * max(splits) else: full_w = batch_w * max(splits) full_h = batch_h * len(splits) y_label_offset = 0 has_horizontal_labels = False if horizontal_labels is not None: horizontal_labels = [str(lbl) for lbl in horizontal_labels] if stack_direction == "horizontal": if len(horizontal_labels) != len(splits): raise Exception("Number of horizontal labels must match number of splits.") else: if len(horizontal_labels) != max(splits): raise Exception("Number of horizontal labels must match maximum split size.") full_h += 60 y_label_offset = 60 has_horizontal_labels = True x_label_offset = 0 has_vertical_labels = False if vertical_labels is not None: vertical_labels = [str(lbl) for lbl in vertical_labels] if stack_direction == "horizontal": if len(vertical_labels) != max(splits): raise Exception("Number of vertical labels must match maximum split size.") else: if len(vertical_labels) != len(splits): raise Exception("Number of vertical labels must match number of splits.") full_w += 60 x_label_offset = 60 has_vertical_labels = True full_image = Image.new("RGB", (full_w, full_h)) batch_idx = 0 if has_horizontal_labels: assert horizontal_labels is not None font = ImageFont.truetype(fm.findfont(fm.FontProperties()), 60) for label_idx, label in enumerate(horizontal_labels): x_offset = (batch_w * label_idx) + x_label_offset draw = ImageDraw.Draw(full_image) draw.rectangle((x_offset, 0, x_offset + batch_w, 60), fill="#ffffff") draw.text((x_offset + (batch_w / 2), 0), label, fill="red", font=font) if has_vertical_labels: assert vertical_labels is not None font = ImageFont.truetype(fm.findfont(fm.FontProperties()), 60) for label_idx, label in enumerate(vertical_labels): y_offset = (batch_h * label_idx) + y_label_offset draw = ImageDraw.Draw(full_image) draw.rectangle((0, y_offset, 60, y_offset + batch_h), fill="#ffffff") draw.text((0, y_offset + (batch_h / 2)), label, fill="red", font=font) for split_idx, split in enumerate(splits): for idx_in_split in range(split): batch_img = Image.new("RGB", (batch_w, batch_h)) batch = batches[batch_idx + idx_in_split] if batch_stack_direction == "horizontal": for img_idx, img in enumerate(batch): x_offset = image_w * img_idx batch_img.paste(tensor2pil(img), (x_offset, 0)) else: for img_idx, img in enumerate(batch): y_offset = image_h * img_idx batch_img.paste(tensor2pil(img), (0, y_offset)) if stack_direction == "horizontal": x_offset = batch_w * split_idx + x_label_offset y_offset = batch_h * idx_in_split + y_label_offset else: x_offset = batch_w * idx_in_split + x_label_offset y_offset = batch_h * split_idx + y_label_offset full_image.paste(batch_img, (x_offset, y_offset)) batch_idx += split return (pil2tensor(full_image),) ```