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from .log import log | |
from .utils import ResizeMode, safe_numpy | |
import numpy as np | |
import torch | |
import cv2 | |
from .utils import get_unique_axis0 | |
from .lvminthin import nake_nms, lvmin_thin | |
MAX_IMAGEGEN_RESOLUTION = 8192 #https://github.com/comfyanonymous/ComfyUI/blob/c910b4a01ca58b04e5d4ab4c747680b996ada02b/nodes.py#L42 | |
RESIZE_MODES = [ResizeMode.RESIZE.value, ResizeMode.INNER_FIT.value, ResizeMode.OUTER_FIT.value] | |
#Port from https://github.com/Mikubill/sd-webui-controlnet/blob/e67e017731aad05796b9615dc6eadce911298ea1/internal_controlnet/external_code.py#L89 | |
class PixelPerfectResolution: | |
def INPUT_TYPES(s): | |
return { | |
"required": { | |
"original_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 = ("INT",) | |
RETURN_NAMES = ("RESOLUTION (INT)", ) | |
FUNCTION = "execute" | |
CATEGORY = "ControlNet Preprocessors" | |
def execute(self, original_image, image_gen_width, image_gen_height, resize_mode): | |
_, raw_H, raw_W, _ = original_image.shape | |
k0 = float(image_gen_height) / float(raw_H) | |
k1 = float(image_gen_width) / float(raw_W) | |
if resize_mode == ResizeMode.OUTER_FIT.value: | |
estimation = min(k0, k1) * float(min(raw_H, raw_W)) | |
else: | |
estimation = max(k0, k1) * float(min(raw_H, raw_W)) | |
log.debug(f"Pixel Perfect Computation:") | |
log.debug(f"resize_mode = {resize_mode}") | |
log.debug(f"raw_H = {raw_H}") | |
log.debug(f"raw_W = {raw_W}") | |
log.debug(f"target_H = {image_gen_height}") | |
log.debug(f"target_W = {image_gen_width}") | |
log.debug(f"estimation = {estimation}") | |
return (int(np.round(estimation)), ) | |
class HintImageEnchance: | |
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 | |
class ImageGenResolutionFromLatent: | |
def INPUT_TYPES(s): | |
return { | |
"required": { "latent": ("LATENT", ) } | |
} | |
RETURN_TYPES = ("INT", "INT") | |
RETURN_NAMES = ("IMAGE_GEN_WIDTH (INT)", "IMAGE_GEN_HEIGHT (INT)") | |
FUNCTION = "execute" | |
CATEGORY = "ControlNet Preprocessors" | |
def execute(self, latent): | |
_, _, H, W = latent["samples"].shape | |
return (W * 8, H * 8) | |
class ImageGenResolutionFromImage: | |
def INPUT_TYPES(s): | |
return { | |
"required": { "image": ("IMAGE", ) } | |
} | |
RETURN_TYPES = ("INT", "INT") | |
RETURN_NAMES = ("IMAGE_GEN_WIDTH (INT)", "IMAGE_GEN_HEIGHT (INT)") | |
FUNCTION = "execute" | |
CATEGORY = "ControlNet Preprocessors" | |
def execute(self, image): | |
_, H, W, _ = image.shape | |
return (W, H) | |
NODE_CLASS_MAPPINGS = { | |
"PixelPerfectResolution": PixelPerfectResolution, | |
"ImageGenResolutionFromImage": ImageGenResolutionFromImage, | |
"ImageGenResolutionFromLatent": ImageGenResolutionFromLatent, | |
"HintImageEnchance": HintImageEnchance | |
} | |
NODE_DISPLAY_NAME_MAPPINGS = { | |
"PixelPerfectResolution": "Pixel Perfect Resolution", | |
"ImageGenResolutionFromImage": "Generation Resolution From Image", | |
"ImageGenResolutionFromLatent": "Generation Resolution From Latent", | |
"HintImageEnchance": "Enchance And Resize Hint Images" | |
} |