# 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 ```python 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 ```