121 lines
3.9 KiB
Markdown
121 lines
3.9 KiB
Markdown
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# Documentation
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- Class name: LoadImageFromUrl
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- Category: Art Venture/Image
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- Output node: False
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这个节点专门用于从URL获取和处理图像，支持处理各种图像格式，并提供保留透明度信息的选项。
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# Input types
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## Required
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- url
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- 指定要加载图像的URL，支持多种来源。
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- Comfy dtype: STRING
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- Python dtype: str
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## Optional
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- keep_alpha_channel
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- 指示是否应保留图像的透明度通道，对于透明度至关重要的图像很有用。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- output_mode
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- 控制图像的输出格式，提供列表和批处理模式之间的选项，以灵活处理加载的图像。
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- Comfy dtype: BOOLEAN
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- Python dtype: str
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# Output types
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- images
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- 加载的图像，如果指定，可能包括透明度信息。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- masks
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- 从图像生成的蒙版，用于进一步的图像处理任务。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- has_image
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- 表示是否成功加载图像的布尔指示符。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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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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class UtilLoadImageFromUrl:
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def __init__(self) -> None:
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self.output_dir = folder_paths.get_temp_directory()
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self.filename_prefix = "TempImageFromUrl"
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"url": ("STRING", {"default": "", "multiline": True, "dynamicPrompts": False}),
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},
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"optional": {
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"keep_alpha_channel": (
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"BOOLEAN",
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{"default": False, "label_on": "enabled", "label_off": "disabled"},
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),
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"output_mode": (
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"BOOLEAN",
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{"default": False, "label_on": "list", "label_off": "batch"},
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),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK", "BOOLEAN")
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OUTPUT_IS_LIST = (True, True, False)
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RETURN_NAMES = ("images", "masks", "has_image")
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CATEGORY = "Art Venture/Image"
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FUNCTION = "load_image"
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def load_image(self, url: str, keep_alpha_channel=False, output_mode=False):
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urls = url.strip().split("\n")
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images, masks = load_images_from_url(urls, keep_alpha_channel)
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if len(images) == 0:
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image = torch.zeros((1, 64, 64, 3), dtype=torch.float32, device="cpu")
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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return ([image], [mask], False)
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previews = []
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np_images = []
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np_masks = []
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for image, mask in zip(images, masks):
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# save image to temp folder
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preview = prepare_image_for_preview(image, self.output_dir, self.filename_prefix)
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image = pil2tensor(image)
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if mask:
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mask = np.array(mask).astype(np.float32) / 255.0
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mask = 1.0 - torch.from_numpy(mask)
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else:
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mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
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previews.append(preview)
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np_images.append(image)
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np_masks.append(mask.unsqueeze(0))
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if output_mode:
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result = (np_images, np_masks, True)
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else:
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has_size_mismatch = False
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if len(np_images) > 1:
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for image in np_images[1:]:
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if image.shape[1] != np_images[0].shape[1] or image.shape[2] != np_images[0].shape[2]:
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has_size_mismatch = True
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break
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if has_size_mismatch:
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raise Exception("To output as batch, images must have the same size. Use list output mode instead.")
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result = ([torch.cat(np_images)], [torch.cat(np_masks)], True)
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return {"ui": {"images": previews}, "result": result}
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```
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