6.5 KiB
6.5 KiB
Documentation
- Class name: ImageScaleRestoreV2
- Category: 😺dzNodes/LayerUtility
- Output node: False
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
V2 将图像或遮罩按宽高比缩放。可设置将缩放后的尺寸按8或者16的倍数取整，可按长边尺寸缩放。
Input types
Required
-
image
- 图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
scale
- 缩放比例。
- Comfy dtype: FLOAT
- Python dtype: float
-
method
- 采样方式。
- Comfy dtype: STRING_ONEOF
- Python dtype: str
- Options:
- lanczos
- bicubic
- hamming
- bilinear
- box
- nearest
-
scale_by
- 缩放方式。
- Comfy dtype: STRING_ONEOF
- Python dtype: str
- Options:
- by_scale
- longest
- shortest
- width
- height
- total_pixel(kilo pixel)
-
scale_by_length
- 缩放长度。
- Comfy dtype: INT
- Python dtype: int
Optional
-
mask
- 蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
original_size
- 原始尺寸。
- Comfy dtype: BOX
- Python dtype: List[int]
Output types
-
image
- 缩放后的图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
mask
- 缩放后的蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
original_size
- 原始尺寸。
- Comfy dtype: BOX
- Python dtype: List[int]
-
width
- 目标宽度。
- Comfy dtype: INT
- Python dtype: int
-
height
- 目标高度。
- Comfy dtype: INT
- Python dtype: int
Usage tips
- Infra type: CPU
Source code
class ImageScaleRestoreV2:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
scale_by_list = ['by_scale', 'longest', 'shortest', 'width', 'height', 'total_pixel(kilo pixel)']
return {
"required": {
"image": ("IMAGE", ), #
"scale": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
"method": (method_mode,),
"scale_by": (scale_by_list,), # 是否按长边缩放
"scale_by_length": ("INT", {"default": 1024, "min": 4, "max": 99999999, "step": 1}),
},
"optional": {
"mask": ("MASK",), #
"original_size": ("BOX",),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "BOX", "INT", "INT")
RETURN_NAMES = ("image", "mask", "original_size", "width", "height",)
FUNCTION = 'image_scale_restore'
CATEGORY = '😺dzNodes/LayerUtility'
def image_scale_restore(self, image, scale, method,
scale_by, scale_by_length,
mask = None, original_size = None
):
l_images = []
l_masks = []
ret_images = []
ret_masks = []
for l in image:
l_images.append(torch.unsqueeze(l, 0))
m = tensor2pil(l)
if m.mode == 'RGBA':
l_masks.append(m.split()[-1])
if mask is not None:
if mask.dim() == 2:
mask = torch.unsqueeze(mask, 0)
l_masks = []
for m in mask:
l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
max_batch = max(len(l_images), len(l_masks))
orig_width, orig_height = tensor2pil(l_images[0]).size
if original_size is not None:
target_width = original_size[0]
target_height = original_size[1]
else:
target_width = int(orig_width * scale)
target_height = int(orig_height * scale)
if scale_by == 'longest':
if orig_width > orig_height:
target_width = scale_by_length
target_height = int(target_width * orig_height / orig_width)
else:
target_height = scale_by_length
target_width = int(target_height * orig_width / orig_height)
if scale_by == 'shortest':
if orig_width < orig_height:
target_width = scale_by_length
target_height = int(target_width * orig_height / orig_width)
else:
target_height = scale_by_length
target_width = int(target_height * orig_width / orig_height)
if scale_by == 'width':
target_width = scale_by_length
target_height = int(target_width * orig_height / orig_width)
if scale_by == 'height':
target_height = scale_by_length
target_width = int(target_height * orig_width / orig_height)
if scale_by == 'total_pixel(kilo pixel)':
r = orig_width / orig_height
target_width = math.sqrt(r * scale_by_length * 1000)
target_height = target_width / r
target_width = int(target_width)
target_height = int(target_height)
if target_width < 4:
target_width = 4
if target_height < 4:
target_height = 4
resize_sampler = Image.LANCZOS
if method == "bicubic":
resize_sampler = Image.BICUBIC
elif method == "hamming":
resize_sampler = Image.HAMMING
elif method == "bilinear":
resize_sampler = Image.BILINEAR
elif method == "box":
resize_sampler = Image.BOX
elif method == "nearest":
resize_sampler = Image.NEAREST
for i in range(max_batch):
_image = l_images[i] if i < len(l_images) else l_images[-1]
_canvas = tensor2pil(_image).convert('RGB')
ret_image = _canvas.resize((target_width, target_height), resize_sampler)
ret_mask = Image.new('L', size=ret_image.size, color='white')
if mask is not None:
_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
ret_mask = _mask.resize((target_width, target_height), resize_sampler)
ret_images.append(pil2tensor(ret_image))
ret_masks.append(image2mask(ret_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0), [orig_width, orig_height], target_width, target_height,)