7.4 KiB
7.4 KiB
Documentation
- Class name: Inference_Core_HintImageEnchance
- Category: ControlNet Preprocessors
- Output node: False
- Repo Ref: https://github.com/comfyanonymous/ComfyUI
Inference_Core_HintImageEnchance节点设计用于预处理图像生成任务的提示图像，根据指定的尺寸和模式对其进行增强和缩放。它支持多种操作，如将图像缩放至指定尺寸范围内、外部和内部拟合，以及应用增强效果，如二值化阈值处理、边缘检测和基于图像alpha通道的修复。
Input types
Required
- hint_image
- 需要处理的提示图像，可以进行增强和缩放以更好地适用于图像生成任务。
- Comfy dtype: IMAGE
- Python dtype: List[torch.Tensor]
- image_gen_width
- 生成图像的目标宽度，用于指导缩放过程。
- Comfy dtype: INT
- Python dtype: int
- image_gen_height
- 生成图像的目标高度，用于指导缩放过程。
- Comfy dtype: INT
- Python dtype: int
- resize_mode
- 要应用的缩放模式，决定了如何调整提示图像以适应目标尺寸。
- Comfy dtype: COMBO[STRING]
- Python dtype: ResizeMode
Output types
- image
- 经过处理的提示图像，根据指定参数进行了增强和缩放，可直接用于图像生成。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Usage tips
- Infra type:
GPU - Common nodes: unknown
Source code
class HintImageEnchance:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"hint_image": ("IMAGE", ),
"image_gen_width": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}),
"image_gen_height": ("INT", {"default": 512, "min": 64, "max": MAX_IMAGEGEN_RESOLUTION, "step": 8}),
#https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L854
"resize_mode": (RESIZE_MODES, {"default": ResizeMode.RESIZE.value})
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "execute"
CATEGORY = "ControlNet Preprocessors"
def execute(self, hint_image, image_gen_width, image_gen_height, resize_mode):
outs = []
for single_hint_image in hint_image:
np_hint_image = np.asarray(single_hint_image * 255., dtype=np.uint8)
if resize_mode == ResizeMode.RESIZE.value:
np_hint_image = self.execute_resize(np_hint_image, image_gen_width, image_gen_height)
elif resize_mode == ResizeMode.OUTER_FIT.value:
np_hint_image = self.execute_outer_fit(np_hint_image, image_gen_width, image_gen_height)
else:
np_hint_image = self.execute_inner_fit(np_hint_image, image_gen_width, image_gen_height)
outs.append(torch.from_numpy(np_hint_image.astype(np.float32) / 255.0))
return (torch.stack(outs, dim=0),)
def execute_resize(self, detected_map, w, h):
detected_map = self.high_quality_resize(detected_map, (w, h))
detected_map = safe_numpy(detected_map)
return detected_map
def execute_outer_fit(self, detected_map, w, h):
old_h, old_w, _ = detected_map.shape
old_w = float(old_w)
old_h = float(old_h)
k0 = float(h) / old_h
k1 = float(w) / old_w
safeint = lambda x: int(np.round(x))
k = min(k0, k1)
borders = np.concatenate([detected_map[0, :, :], detected_map[-1, :, :], detected_map[:, 0, :], detected_map[:, -1, :]], axis=0)
high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype)
if len(high_quality_border_color) == 4:
# Inpaint hijack
high_quality_border_color[3] = 255
high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
detected_map = self.high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
new_h, new_w, _ = detected_map.shape
pad_h = max(0, (h - new_h) // 2)
pad_w = max(0, (w - new_w) // 2)
high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map
detected_map = high_quality_background
detected_map = safe_numpy(detected_map)
return detected_map
def execute_inner_fit(self, detected_map, w, h):
old_h, old_w, _ = detected_map.shape
old_w = float(old_w)
old_h = float(old_h)
k0 = float(h) / old_h
k1 = float(w) / old_w
safeint = lambda x: int(np.round(x))
k = max(k0, k1)
detected_map = self.high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
new_h, new_w, _ = detected_map.shape
pad_h = max(0, (new_h - h) // 2)
pad_w = max(0, (new_w - w) // 2)
detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w]
detected_map = safe_numpy(detected_map)
return detected_map
def high_quality_resize(self, x, size):
# Written by lvmin
# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
inpaint_mask = None
if x.ndim == 3 and x.shape[2] == 4:
inpaint_mask = x[:, :, 3]
x = x[:, :, 0:3]
if x.shape[0] != size[1] or x.shape[1] != size[0]:
new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
unique_color_count = len(get_unique_axis0(x.reshape(-1, x.shape[2])))
is_one_pixel_edge = False
is_binary = False
if unique_color_count == 2:
is_binary = np.min(x) < 16 and np.max(x) > 240
if is_binary:
xc = x
xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
one_pixel_edge_count = np.where(xc < x)[0].shape[0]
all_edge_count = np.where(x > 127)[0].shape[0]
is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
if 2 < unique_color_count < 200:
interpolation = cv2.INTER_NEAREST
elif new_size_is_smaller:
interpolation = cv2.INTER_AREA
else:
interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS
y = cv2.resize(x, size, interpolation=interpolation)
if inpaint_mask is not None:
inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
if is_binary:
y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
if is_one_pixel_edge:
y = nake_nms(y)
_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
y = lvmin_thin(y, prunings=new_size_is_bigger)
else:
_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
y = np.stack([y] * 3, axis=2)
else:
y = x
if inpaint_mask is not None:
inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
y = np.concatenate([y, inpaint_mask], axis=2)
return y