96 lines
3.0 KiB
Markdown
96 lines
3.0 KiB
Markdown
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# Documentation
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- Class name: JWSaveImageSequence
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- Category: jamesWalker55
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- Output node: True
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JWSaveImageSequence节点设计用于批量保存图像序列到指定路径,允许自定义文件命名、索引编号,并提供覆盖现有文件的选项。它可以有组织地输出图像数据,并支持为每个保存的图像添加额外的元数据和提示信息。
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# Input types
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## Required
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- images
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- 要保存的图像张量。这个集合代表将在批处理操作中保存的图像序列。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- path_pattern
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- 定义保存序列中每个图像的文件命名约定和路径的字符串模式。支持使用格式化语法来包含索引。
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- Comfy dtype: STRING
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- Python dtype: str
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- start_index
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- 用于命名已保存图像文件的起始索引,它与路径模式结合使用来生成文件名。
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- Comfy dtype: INT
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- Python dtype: int
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- overwrite
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- 指示是否应覆盖目标保存位置的现有文件的字符串。接受'true'或'false'作为值。
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- Comfy dtype: ['false', 'true']
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- Python dtype: str
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# Output types
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该节点没有输出类型
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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@register_node("JWLoadImageSequence", "Batch Load Image Sequence")
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class _:
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CATEGORY = "jamesWalker55"
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INPUT_TYPES = lambda: {
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"required": {
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"path_pattern": (
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"STRING",
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{"default": "./frame{:06d}.png", "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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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: bool = ignore_missing_images == "true"
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# generate image paths to load
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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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# remove 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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# early check for missing images
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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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imgs = []
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for path in image_paths:
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img = load_image(path)
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# img.shape => torch.Size([1, 768, 768, 3])
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imgs.append(img)
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imgs = torch.cat(imgs, dim=0)
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return (imgs,)
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```
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