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This commit is contained in:
lllyasviel
@@ -1,3 +1,13 @@
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from typing import Optional
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from modules import processing
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from lib_controlnet import external_code
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from modules_forge.forge_util import HWC3
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from PIL import Image, ImageFilter, ImageOps
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from lib_controlnet.lvminthin import lvmin_thin, nake_nms
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import torch
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import torch
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import os
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import os
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import functools
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import functools
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@@ -177,4 +187,224 @@ def align_dim_latent(x: int) -> int:
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""" Align the pixel dimension (w/h) to latent dimension.
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""" Align the pixel dimension (w/h) to latent dimension.
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Stable diffusion 1:8 ratio for latent/pixel, i.e.,
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Stable diffusion 1:8 ratio for latent/pixel, i.e.,
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1 latent unit == 8 pixel unit."""
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1 latent unit == 8 pixel unit."""
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return (x // 8) * 8
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return (x // 8) * 8
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def image_dict_from_any(image) -> Optional[Dict[str, np.ndarray]]:
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if image is None:
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return None
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if isinstance(image, (tuple, list)):
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image = {'image': image[0], 'mask': image[1]}
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elif not isinstance(image, dict):
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image = {'image': image, 'mask': None}
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else: # type(image) is dict
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# copy to enable modifying the dict and prevent response serialization error
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image = dict(image)
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if isinstance(image['image'], str):
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if os.path.exists(image['image']):
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image['image'] = np.array(Image.open(image['image'])).astype('uint8')
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elif image['image']:
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image['image'] = external_code.to_base64_nparray(image['image'])
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else:
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image['image'] = None
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# If there is no image, return image with None image and None mask
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if image['image'] is None:
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image['mask'] = None
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return image
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if 'mask' not in image or image['mask'] is None:
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image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
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elif isinstance(image['mask'], str):
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if os.path.exists(image['mask']):
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image['mask'] = np.array(Image.open(image['mask'])).astype('uint8')
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elif image['mask']:
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image['mask'] = external_code.to_base64_nparray(image['mask'])
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else:
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image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
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return image
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def prepare_mask(
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mask: Image.Image, p: processing.StableDiffusionProcessing
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) -> Image.Image:
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"""
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Prepare an image mask for the inpainting process.
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This function takes as input a PIL Image object and an instance of the
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StableDiffusionProcessing class, and performs the following steps to prepare the mask:
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1. Convert the mask to grayscale (mode "L").
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2. If the 'inpainting_mask_invert' attribute of the processing instance is True,
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invert the mask colors.
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3. If the 'mask_blur' attribute of the processing instance is greater than 0,
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apply a Gaussian blur to the mask with a radius equal to 'mask_blur'.
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Args:
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mask (Image.Image): The input mask as a PIL Image object.
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p (processing.StableDiffusionProcessing): An instance of the StableDiffusionProcessing class
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containing the processing parameters.
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Returns:
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mask (Image.Image): The prepared mask as a PIL Image object.
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"""
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mask = mask.convert("L")
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if getattr(p, "inpainting_mask_invert", False):
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mask = ImageOps.invert(mask)
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if hasattr(p, 'mask_blur_x'):
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if getattr(p, "mask_blur_x", 0) > 0:
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np_mask = np.array(mask)
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kernel_size = 2 * int(2.5 * p.mask_blur_x + 0.5) + 1
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np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), p.mask_blur_x)
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mask = Image.fromarray(np_mask)
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if getattr(p, "mask_blur_y", 0) > 0:
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np_mask = np.array(mask)
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kernel_size = 2 * int(2.5 * p.mask_blur_y + 0.5) + 1
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np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), p.mask_blur_y)
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mask = Image.fromarray(np_mask)
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else:
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if getattr(p, "mask_blur", 0) > 0:
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mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
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return mask
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def set_numpy_seed(p: processing.StableDiffusionProcessing) -> Optional[int]:
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"""
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Set the random seed for NumPy based on the provided parameters.
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Args:
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p (processing.StableDiffusionProcessing): The instance of the StableDiffusionProcessing class.
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Returns:
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Optional[int]: The computed random seed if successful, or None if an exception occurs.
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This function sets the random seed for NumPy using the seed and subseed values from the given instance of
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StableDiffusionProcessing. If either seed or subseed is -1, it uses the first value from `all_seeds`.
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Otherwise, it takes the maximum of the provided seed value and 0.
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The final random seed is computed by adding the seed and subseed values, applying a bitwise AND operation
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with 0xFFFFFFFF to ensure it fits within a 32-bit integer.
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"""
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try:
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tmp_seed = int(p.all_seeds[0] if p.seed == -1 else max(int(p.seed), 0))
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tmp_subseed = int(p.all_seeds[0] if p.subseed == -1 else max(int(p.subseed), 0))
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seed = (tmp_seed + tmp_subseed) & 0xFFFFFFFF
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np.random.seed(seed)
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return seed
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except Exception as e:
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logger.warning(e)
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logger.warning('Warning: Failed to use consistent random seed.')
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return None
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def safe_numpy(x):
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# A very safe method to make sure that Apple/Mac works
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y = x
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# below is very boring but do not change these. If you change these Apple or Mac may fail.
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y = y.copy()
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y = np.ascontiguousarray(y)
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y = y.copy()
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return y
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def high_quality_resize(x, size):
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# Written by lvmin
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# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
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inpaint_mask = None
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if x.ndim == 3 and x.shape[2] == 4:
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inpaint_mask = x[:, :, 3]
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x = x[:, :, 0:3]
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if x.shape[0] != size[1] or x.shape[1] != size[0]:
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new_size_is_smaller = (size[0] * size[1]) < (x.shape[0] * x.shape[1])
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new_size_is_bigger = (size[0] * size[1]) > (x.shape[0] * x.shape[1])
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unique_color_count = len(get_unique_axis0(x.reshape(-1, x.shape[2])))
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is_one_pixel_edge = False
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is_binary = False
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if unique_color_count == 2:
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is_binary = np.min(x) < 16 and np.max(x) > 240
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if is_binary:
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xc = x
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xc = cv2.erode(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
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xc = cv2.dilate(xc, np.ones(shape=(3, 3), dtype=np.uint8), iterations=1)
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one_pixel_edge_count = np.where(xc < x)[0].shape[0]
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all_edge_count = np.where(x > 127)[0].shape[0]
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is_one_pixel_edge = one_pixel_edge_count * 2 > all_edge_count
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if 2 < unique_color_count < 200:
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interpolation = cv2.INTER_NEAREST
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elif new_size_is_smaller:
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interpolation = cv2.INTER_AREA
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else:
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interpolation = cv2.INTER_CUBIC # Must be CUBIC because we now use nms. NEVER CHANGE THIS
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y = cv2.resize(x, size, interpolation=interpolation)
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if inpaint_mask is not None:
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inpaint_mask = cv2.resize(inpaint_mask, size, interpolation=interpolation)
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if is_binary:
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y = np.mean(y.astype(np.float32), axis=2).clip(0, 255).astype(np.uint8)
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if is_one_pixel_edge:
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y = nake_nms(y)
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_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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y = lvmin_thin(y, prunings=new_size_is_bigger)
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else:
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_, y = cv2.threshold(y, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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y = np.stack([y] * 3, axis=2)
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else:
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y = x
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if inpaint_mask is not None:
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inpaint_mask = (inpaint_mask > 127).astype(np.float32) * 255.0
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inpaint_mask = inpaint_mask[:, :, None].clip(0, 255).astype(np.uint8)
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y = np.concatenate([y, inpaint_mask], axis=2)
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return y
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def crop_and_resize_image(detected_map, resize_mode, h, w):
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if resize_mode == external_code.ResizeMode.RESIZE:
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detected_map = high_quality_resize(detected_map, (w, h))
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detected_map = safe_numpy(detected_map)
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return detected_map
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old_h, old_w, _ = detected_map.shape
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old_w = float(old_w)
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old_h = float(old_h)
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k0 = float(h) / old_h
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k1 = float(w) / old_w
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safeint = lambda x: int(np.round(x))
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if resize_mode == external_code.ResizeMode.OUTER_FIT:
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k = min(k0, k1)
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borders = np.concatenate([detected_map[0, :, :], detected_map[-1, :, :], detected_map[:, 0, :], detected_map[:, -1, :]], axis=0)
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high_quality_border_color = np.median(borders, axis=0).astype(detected_map.dtype)
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if len(high_quality_border_color) == 4:
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# Inpaint hijack
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high_quality_border_color[3] = 255
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high_quality_background = np.tile(high_quality_border_color[None, None], [h, w, 1])
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detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
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new_h, new_w, _ = detected_map.shape
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pad_h = max(0, (h - new_h) // 2)
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pad_w = max(0, (w - new_w) // 2)
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high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = detected_map
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detected_map = high_quality_background
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detected_map = safe_numpy(detected_map)
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return detected_map
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else:
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k = max(k0, k1)
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detected_map = high_quality_resize(detected_map, (safeint(old_w * k), safeint(old_h * k)))
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new_h, new_w, _ = detected_map.shape
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pad_h = max(0, (new_h - h) // 2)
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pad_w = max(0, (new_w - w) // 2)
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detected_map = detected_map[pad_h:pad_h+h, pad_w:pad_w+w]
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detected_map = safe_numpy(detected_map)
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return detected_map
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@@ -2,13 +2,12 @@ import os
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from copy import copy
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from copy import copy
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from typing import Dict, Optional, Tuple, List, Union
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from typing import Dict, Optional, Tuple, List, Union
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import modules.scripts as scripts
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import modules.scripts as scripts
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from modules import shared, devices, script_callbacks, processing, masking, images
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from modules import shared, script_callbacks, processing, masking, images
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from modules.api.api import decode_base64_to_image
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from modules.api.api import decode_base64_to_image
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import gradio as gr
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import gradio as gr
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from einops import rearrange
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from lib_controlnet import global_state, external_code
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from lib_controlnet import global_state, external_code, utils
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from lib_controlnet.utils import align_dim_latent, image_dict_from_any, set_numpy_seed, crop_and_resize_image, prepare_mask
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from lib_controlnet.utils import get_unique_axis0, align_dim_latent
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from lib_controlnet.enums import StableDiffusionVersion, HiResFixOption
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from lib_controlnet.enums import StableDiffusionVersion, HiResFixOption
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from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
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from lib_controlnet.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
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from lib_controlnet.controlnet_ui.photopea import Photopea
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from lib_controlnet.controlnet_ui.photopea import Photopea
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@@ -22,8 +21,7 @@ import numpy as np
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import torch
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import torch
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import functools
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import functools
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from PIL import Image, ImageFilter, ImageOps
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from PIL import Image
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from lib_controlnet.lvminthin import lvmin_thin, nake_nms
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from modules_forge.shared import try_load_supported_control_model
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from modules_forge.shared import try_load_supported_control_model
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@@ -41,132 +39,6 @@ def cached_controlnet_loader(filename):
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return try_load_supported_control_model(filename)
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return try_load_supported_control_model(filename)
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def image_dict_from_any(image) -> Optional[Dict[str, np.ndarray]]:
|
|
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if image is None:
|
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return None
|
|
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|
|
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if isinstance(image, (tuple, list)):
|
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image = {'image': image[0], 'mask': image[1]}
|
|
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elif not isinstance(image, dict):
|
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image = {'image': image, 'mask': None}
|
|
||||||
else: # type(image) is dict
|
|
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# copy to enable modifying the dict and prevent response serialization error
|
|
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image = dict(image)
|
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|
|
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if isinstance(image['image'], str):
|
|
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if os.path.exists(image['image']):
|
|
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image['image'] = np.array(Image.open(image['image'])).astype('uint8')
|
|
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elif image['image']:
|
|
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image['image'] = external_code.to_base64_nparray(image['image'])
|
|
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else:
|
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image['image'] = None
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|
|
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# If there is no image, return image with None image and None mask
|
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if image['image'] is None:
|
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image['mask'] = None
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return image
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|
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|
|
||||||
if 'mask' not in image or image['mask'] is None:
|
|
||||||
image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
|
|
||||||
elif isinstance(image['mask'], str):
|
|
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if os.path.exists(image['mask']):
|
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image['mask'] = np.array(Image.open(image['mask'])).astype('uint8')
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|
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elif image['mask']:
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image['mask'] = external_code.to_base64_nparray(image['mask'])
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else:
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image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
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return image
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|
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|
||||||
|
|
||||||
def prepare_mask(
|
|
||||||
mask: Image.Image, p: processing.StableDiffusionProcessing
|
|
||||||
) -> Image.Image:
|
|
||||||
"""
|
|
||||||
Prepare an image mask for the inpainting process.
|
|
||||||
|
|
||||||
This function takes as input a PIL Image object and an instance of the
|
|
||||||
StableDiffusionProcessing class, and performs the following steps to prepare the mask:
|
|
||||||
|
|
||||||
1. Convert the mask to grayscale (mode "L").
|
|
||||||
2. If the 'inpainting_mask_invert' attribute of the processing instance is True,
|
|
||||||
invert the mask colors.
|
|
||||||
3. If the 'mask_blur' attribute of the processing instance is greater than 0,
|
|
||||||
apply a Gaussian blur to the mask with a radius equal to 'mask_blur'.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
mask (Image.Image): The input mask as a PIL Image object.
|
|
||||||
p (processing.StableDiffusionProcessing): An instance of the StableDiffusionProcessing class
|
|
||||||
containing the processing parameters.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
mask (Image.Image): The prepared mask as a PIL Image object.
|
|
||||||
"""
|
|
||||||
mask = mask.convert("L")
|
|
||||||
if getattr(p, "inpainting_mask_invert", False):
|
|
||||||
mask = ImageOps.invert(mask)
|
|
||||||
|
|
||||||
if hasattr(p, 'mask_blur_x'):
|
|
||||||
if getattr(p, "mask_blur_x", 0) > 0:
|
|
||||||
np_mask = np.array(mask)
|
|
||||||
kernel_size = 2 * int(2.5 * p.mask_blur_x + 0.5) + 1
|
|
||||||
np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), p.mask_blur_x)
|
|
||||||
mask = Image.fromarray(np_mask)
|
|
||||||
if getattr(p, "mask_blur_y", 0) > 0:
|
|
||||||
np_mask = np.array(mask)
|
|
||||||
kernel_size = 2 * int(2.5 * p.mask_blur_y + 0.5) + 1
|
|
||||||
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), p.mask_blur_y)
|
|
||||||
mask = Image.fromarray(np_mask)
|
|
||||||
else:
|
|
||||||
if getattr(p, "mask_blur", 0) > 0:
|
|
||||||
mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
|
|
||||||
|
|
||||||
return mask
|
|
||||||
|
|
||||||
|
|
||||||
def set_numpy_seed(p: processing.StableDiffusionProcessing) -> Optional[int]:
|
|
||||||
"""
|
|
||||||
Set the random seed for NumPy based on the provided parameters.
|
|
||||||
|
|
||||||
Args:
|
|
||||||
p (processing.StableDiffusionProcessing): The instance of the StableDiffusionProcessing class.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
Optional[int]: The computed random seed if successful, or None if an exception occurs.
|
|
||||||
|
|
||||||
This function sets the random seed for NumPy using the seed and subseed values from the given instance of
|
|
||||||
StableDiffusionProcessing. If either seed or subseed is -1, it uses the first value from `all_seeds`.
|
|
||||||
Otherwise, it takes the maximum of the provided seed value and 0.
|
|
||||||
|
|
||||||
The final random seed is computed by adding the seed and subseed values, applying a bitwise AND operation
|
|
||||||
with 0xFFFFFFFF to ensure it fits within a 32-bit integer.
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
tmp_seed = int(p.all_seeds[0] if p.seed == -1 else max(int(p.seed), 0))
|
|
||||||
tmp_subseed = int(p.all_seeds[0] if p.subseed == -1 else max(int(p.subseed), 0))
|
|
||||||
seed = (tmp_seed + tmp_subseed) & 0xFFFFFFFF
|
|
||||||
np.random.seed(seed)
|
|
||||||
return seed
|
|
||||||
except Exception as e:
|
|
||||||
logger.warning(e)
|
|
||||||
logger.warning('Warning: Failed to use consistent random seed.')
|
|
||||||
return None
|
|
||||||
|
|
||||||
|
|
||||||
def get_pytorch_control(x: np.ndarray) -> torch.Tensor:
|
|
||||||
# A very safe method to make sure that Apple/Mac works
|
|
||||||
y = x
|
|
||||||
|
|
||||||
# below is very boring but do not change these. If you change these Apple or Mac may fail.
|
|
||||||
y = torch.from_numpy(y)
|
|
||||||
y = y.float() / 255.0
|
|
||||||
y = rearrange(y, 'h w c -> 1 c h w')
|
|
||||||
y = y.clone()
|
|
||||||
y = y.to(devices.get_device_for("controlnet"))
|
|
||||||
y = y.clone()
|
|
||||||
return y
|
|
||||||
|
|
||||||
|
|
||||||
class ControlNetCachedParameters:
|
class ControlNetCachedParameters:
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.control_image = None
|
self.control_image = None
|
||||||
@@ -271,118 +143,6 @@ class ControlNetForForgeOfficial(scripts.Script):
|
|||||||
|
|
||||||
return unit
|
return unit
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def detectmap_proc(detected_map, module, resize_mode, h, w):
|
|
||||||
|
|
||||||
if 'inpaint' in module:
|
|
||||||
detected_map = detected_map.astype(np.float32)
|
|
||||||
else:
|
|
||||||
detected_map = HWC3(detected_map)
|
|
||||||
|
|
||||||
def safe_numpy(x):
|
|
||||||
# A very safe method to make sure that Apple/Mac works
|
|
||||||
y = x
|
|
||||||
|
|
||||||
# below is very boring but do not change these. If you change these Apple or Mac may fail.
|
|
||||||
y = y.copy()
|
|
||||||
y = np.ascontiguousarray(y)
|
|
||||||
y = y.copy()
|
|
||||||
return y
|
|
||||||
|
|
||||||
def high_quality_resize(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
|
|
||||||
|
|
||||||
if resize_mode == external_code.ResizeMode.RESIZE:
|
|
||||||
detected_map = high_quality_resize(detected_map, (w, h))
|
|
||||||
detected_map = safe_numpy(detected_map)
|
|
||||||
return get_pytorch_control(detected_map), detected_map
|
|
||||||
|
|
||||||
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))
|
|
||||||
|
|
||||||
if resize_mode == external_code.ResizeMode.OUTER_FIT:
|
|
||||||
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 = 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 get_pytorch_control(detected_map), detected_map
|
|
||||||
else:
|
|
||||||
k = max(k0, k1)
|
|
||||||
detected_map = 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 get_pytorch_control(detected_map), detected_map
|
|
||||||
|
|
||||||
def get_enabled_units(self, p):
|
def get_enabled_units(self, p):
|
||||||
units = external_code.get_all_units_in_processing(p)
|
units = external_code.get_all_units_in_processing(p)
|
||||||
if len(units) == 0:
|
if len(units) == 0:
|
||||||
@@ -822,7 +582,7 @@ class ControlNetForForgeOfficial(scripts.Script):
|
|||||||
|
|
||||||
def process_unit_after_click_generate(self, p, unit, params, *args, **kwargs):
|
def process_unit_after_click_generate(self, p, unit, params, *args, **kwargs):
|
||||||
h, w, hr_y, hr_x = self.get_target_dimensions(p)
|
h, w, hr_y, hr_x = self.get_target_dimensions(p)
|
||||||
|
|
||||||
has_high_res_fix = (
|
has_high_res_fix = (
|
||||||
isinstance(p, StableDiffusionProcessingTxt2Img)
|
isinstance(p, StableDiffusionProcessingTxt2Img)
|
||||||
and getattr(p, 'enable_hr', False)
|
and getattr(p, 'enable_hr', False)
|
||||||
@@ -856,6 +616,13 @@ class ControlNetForForgeOfficial(scripts.Script):
|
|||||||
|
|
||||||
detected_map_is_image = detected_map.ndim == 3 and detected_map.shape[2] < 5
|
detected_map_is_image = detected_map.ndim == 3 and detected_map.shape[2] < 5
|
||||||
|
|
||||||
|
if detected_map_is_image:
|
||||||
|
control, detected_map = Script.detectmap_proc(detected_map, unit.module, resize_mode, h, w)
|
||||||
|
store_detected_map(detected_map, unit.module)
|
||||||
|
else:
|
||||||
|
control = detected_map
|
||||||
|
store_detected_map(input_image, unit.module)
|
||||||
|
|
||||||
return
|
return
|
||||||
|
|
||||||
def process_unit_before_every_sampling(self, p, unit, params, *args, **kwargs):
|
def process_unit_before_every_sampling(self, p, unit, params, *args, **kwargs):
|
||||||
|
|||||||
Reference in New Issue
Block a user