128 lines
4.5 KiB
Markdown
128 lines
4.5 KiB
Markdown
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# Documentation
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- Class name: Empty Images
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- Category: List Stuff
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- Output node: True
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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Empty Images 节点设计用于生成一批具有指定尺寸和颜色的空白图像。它作为一个基础工具，用于在各种图像处理和生成任务中创建占位符或背景图像。
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# Input types
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## Optional
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- num_images
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- 指定要生成的图像总数。该参数可用于在生成图像时控制整体输出数量。
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- Comfy dtype: INT
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- Python dtype: Optional[List[int]]
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- splits
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- 定义如何将图像总数划分为单独的批次或组。这可用于在整体批次内创建不同的图像集。
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- Comfy dtype: INT
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- Python dtype: Optional[List[int]]
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- batch_size
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- 确定单个批次中要生成的图像数量。这允许一次创建多个图像，从而优化处理时间。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- Image
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- 输出是一批具有指定尺寸和颜色的图像。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class EmptyImages:
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(s) -> Dict[str, Dict[str, Any]]:
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return {
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"required": {},
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"optional": {
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"num_images": ("INT", {"forceInput": True, "min": 1}),
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"splits": ("INT", {"forceInput": True, "min": 1}),
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"batch_size": ("INT", {"default": 1, "min": 1}),
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}
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}
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RELOAD_INST = True
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("Image",)
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INPUT_IS_LIST = (True,)
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OUTPUT_IS_LIST = (True,)
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OUTPUT_NODE = True
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FUNCTION = "generate_empty_images"
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CATEGORY = "List Stuff"
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def generate_empty_images(
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self,
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num_images: Optional[List[int]] = None,
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splits: Optional[List[int]] = None,
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batch_size: Optional[List[int]] = None,
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) -> Tuple[List[Tensor]]:
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if batch_size is None:
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batch_size = [1]
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else:
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if len(batch_size) != 1:
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raise Exception("Only single batch size supported.")
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if num_images is None and splits is None:
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raise Exception("Must provide either num_images or splits.")
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if num_images is not None and len(num_images) != 1:
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raise Exception("Only single num_images supported.")
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if num_images is not None and splits is None:
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# If splits is None, then all images are in one split
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splits = [num_images[0]]
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if num_images is None and splits is not None:
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# If num_images is None, then it should be the sum of all splits
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num_images = [sum(splits)]
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if num_images is not None and splits is not None:
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if len(splits) == 1:
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# Fill splits with same value enough times to sum to num_images
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fills = int(num_images[0] / splits[0])
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splits = [splits[0]] * fills
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if sum(splits) != num_images[0]:
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splits.append(num_images[0] - sum(splits))
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else:
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if sum(splits) != num_images[0]:
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raise Exception("Sum of splits must match number of images.")
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if splits is None:
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raise ValueError("Unexpected error: Splits is None")
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ret_images: List[Tensor] = []
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for split_idx, split in enumerate(splits):
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# Rotate between fully dynamic range of colors
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base_color = (
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50 + (split_idx * 45) % 200, # Cycle between 50 and 250
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30 + (split_idx * 75) % 200,
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10 + (split_idx * 105) % 200,
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)
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print(f"Splits: {split} | Base Color: {base_color}")
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for _ in range(split):
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batch_tensor = torch.zeros(batch_size[0], 512, 512, 3)
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for batch_idx in range(batch_size[0]):
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batch_color = (
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(base_color[0] + int(((255 - base_color[0]) / batch_size[0]) * batch_idx)),
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(base_color[1] + int(((255 - base_color[1]) / batch_size[0]) * batch_idx)),
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(base_color[2] + int(((255 - base_color[2]) / batch_size[0]) * batch_idx)),
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)
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image = Image.new("RGB", (512, 512), color=batch_color)
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batch_tensor[batch_idx] = pil2tensor(image)
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ret_images.append(batch_tensor)
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return (ret_images,)
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```
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