Method load_image

nodes.py:1737–1779

(self, image)

    FUNCTION = "load_image"

    def load_image(self, image):
        image_path = folder_paths.get_annotated_filepath(image)

        dtype = comfy.model_management.intermediate_dtype()
        device = comfy.model_management.intermediate_device()

        components = InputImpl.VideoFromFile(image_path).get_components()
        if components.images.shape[0] > 0:
            return (components.images.to(device=device, dtype=dtype), (1.0 - components.alpha[..., -1]).to(device=device, dtype=dtype) if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=dtype, device=device))

        # This code is left here to handle animated webp which pyav does not support loading
        img = node_helpers.pillow(Image.open, image_path)

        output_images = []
        output_masks = []
        w, h = None, None

        for i in ImageSequence.Iterator(img):
            i = node_helpers.pillow(ImageOps.exif_transpose, i)

            image = i.convert("RGB")

            if len(output_images) == 0:
                w = image.size[0]
                h = image.size[1]

            if image.size[0] != w or image.size[1] != h:
                continue

            image = np.array(image).astype(np.float32) / 255.0
            image = torch.from_numpy(image)[None,]
            if 'A' in i.getbands():
                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
                mask = 1. - torch.from_numpy(mask)
            else:
                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
            output_images.append(image.to(dtype=dtype))
            output_masks.append(mask.unsqueeze(0).to(dtype=dtype))

        output_image = torch.cat(output_images, dim=0)
        output_mask = torch.cat(output_masks, dim=0)

        return (output_image.to(device=device, dtype=dtype), output_mask.to(device=device, dtype=dtype))

    @classmethod
    def IS_CHANGED(s, image):