Method prep_image

IPAdapterPlus.py:1546–1591

(self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0)

    CATEGORY = "ipadapter/utils"

    def prep_image(self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0):
        size = (224, 224)
        _, oh, ow, _ = image.shape
        output = image.permute([0,3,1,2])

        if crop_position == "pad":
            if oh != ow:
                if oh > ow:
                    pad = (oh - ow) // 2
                    pad = (pad, 0, pad, 0)
                elif ow > oh:
                    pad = (ow - oh) // 2
                    pad = (0, pad, 0, pad)
                output = T.functional.pad(output, pad, fill=0)
        else:
            crop_size = min(oh, ow)
            x = (ow-crop_size) // 2
            y = (oh-crop_size) // 2
            if "top" in crop_position:
                y = 0
            elif "bottom" in crop_position:
                y = oh-crop_size
            elif "left" in crop_position:
                x = 0
            elif "right" in crop_position:
                x = ow-crop_size

            x2 = x+crop_size
            y2 = y+crop_size

            output = output[:, :, y:y2, x:x2]

        imgs = []
        for img in output:
            img = T.ToPILImage()(img) # using PIL for better results
            img = img.resize(size, resample=Image.Resampling[interpolation])
            imgs.append(T.ToTensor()(img))
        output = torch.stack(imgs, dim=0)
        del imgs, img

        if sharpening > 0:
            output = contrast_adaptive_sharpening(output, sharpening)

        output = output.permute([0,2,3,1])

        return (output, )

class IPAdapterSaveEmbeds:
    def __init__(self):