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import gc
import os
import logging
import re
from collections import OrderedDict
from copy import copy
from typing import Dict, Optional, Tuple
import modules.scripts as scripts
from modules import shared, devices, script_callbacks, processing, masking, images
import gradio as gr
import time
from einops import rearrange
from scripts import global_state, hook, external_code, processor, batch_hijack, controlnet_version, utils
from scripts.controlnet_lora import bind_control_lora, unbind_control_lora
from scripts.processor import *
from scripts.adapter import Adapter, StyleAdapter, Adapter_light
from scripts.controlnet_lllite import PlugableControlLLLite, clear_all_lllite
from scripts.controlmodel_ipadapter import PlugableIPAdapter, clear_all_ip_adapter
from scripts.utils import load_state_dict, get_unique_axis0
from scripts.hook import ControlParams, UnetHook, ControlModelType, HackedImageRNG
from scripts.controlnet_ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit
from scripts.logging import logger
from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img
from modules.images import save_image
from scripts.infotext import Infotext
import cv2
import numpy as np
import torch
from pathlib import Path
from PIL import Image, ImageFilter, ImageOps
from scripts.lvminthin import lvmin_thin, nake_nms
from scripts.processor import model_free_preprocessors
from scripts.controlnet_model_guess import build_model_by_guess
gradio_compat = True
try:
from distutils.version import LooseVersion
from importlib_metadata import version
if LooseVersion(version("gradio")) < LooseVersion("3.10"):
gradio_compat = False
except ImportError:
pass
# Gradio 3.32 bug fix
import tempfile
gradio_tempfile_path = os.path.join(tempfile.gettempdir(), 'gradio')
os.makedirs(gradio_tempfile_path, exist_ok=True)
def clear_all_secondary_control_models():
clear_all_lllite()
clear_all_ip_adapter()
def find_closest_lora_model_name(search: str):
if not search:
return None
if search in global_state.cn_models:
return search
search = search.lower()
if search in global_state.cn_models_names:
return global_state.cn_models_names.get(search)
applicable = [name for name in global_state.cn_models_names.keys()
if search in name.lower()]
if not applicable:
return None
applicable = sorted(applicable, key=lambda name: len(name))
return global_state.cn_models_names[applicable[0]]
def swap_img2img_pipeline(p: processing.StableDiffusionProcessingImg2Img):
p.__class__ = processing.StableDiffusionProcessingTxt2Img
dummy = processing.StableDiffusionProcessingTxt2Img()
for k,v in dummy.__dict__.items():
if hasattr(p, k):
continue
setattr(p, k, v)
global_state.update_cn_models()
def image_dict_from_any(image) -> Optional[Dict[str, np.ndarray]]:
if image is None:
return None
if isinstance(image, (tuple, list)):
image = {'image': image[0], 'mask': image[1]}
elif not isinstance(image, dict):
image = {'image': image, 'mask': None}
else: # type(image) is dict
# copy to enable modifying the dict and prevent response serialization error
image = dict(image)
if isinstance(image['image'], str):
if os.path.exists(image['image']):
image['image'] = np.array(Image.open(image['image'])).astype('uint8')
elif image['image']:
image['image'] = external_code.to_base64_nparray(image['image'])
else:
image['image'] = None
# If there is no image, return image with None image and None mask
if image['image'] is None:
image['mask'] = None
return image
if isinstance(image['mask'], str):
if os.path.exists(image['mask']):
image['mask'] = np.array(Image.open(image['mask'])).astype('uint8')
elif image['mask']:
image['mask'] = external_code.to_base64_nparray(image['mask'])
else:
image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
elif image['mask'] is None:
image['mask'] = np.zeros_like(image['image'], dtype=np.uint8)
return image
def image_has_mask(input_image: np.ndarray) -> bool:
"""
Determine if an image has an alpha channel (mask) that is not empty.
The function checks if the input image has three dimensions (height, width, channels),
and if the third dimension (channel dimension) is of size 4 (presumably RGB + alpha).
Then it checks if the maximum value in the alpha channel is greater than 127. This is
presumably to check if there is any non-transparent (or semi-transparent) pixel in the
image. A pixel is considered non-transparent if its alpha value is above 127.
Args:
input_image (np.ndarray): A 3D numpy array representing an image. The dimensions
should represent [height, width, channels].
Returns:
bool: True if the image has a non-empty alpha channel, False otherwise.
"""
return (
input_image.ndim == 3 and
input_image.shape[2] == 4 and
np.max(input_image[:, :, 3]) > 127
)
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
class Script(scripts.Script, metaclass=(
utils.TimeMeta if logger.level == logging.DEBUG else type)):
model_cache = OrderedDict()
def __init__(self) -> None:
super().__init__()
self.latest_network = None
self.preprocessor = global_state.cache_preprocessors(global_state.cn_preprocessor_modules)
self.unloadable = global_state.cn_preprocessor_unloadable
self.input_image = None
self.latest_model_hash = ""
self.enabled_units = []
self.detected_map = []
self.post_processors = []
self.noise_modifier = None
batch_hijack.instance.process_batch_callbacks.append(self.batch_tab_process)
batch_hijack.instance.process_batch_each_callbacks.append(self.batch_tab_process_each)
batch_hijack.instance.postprocess_batch_each_callbacks.insert(0, self.batch_tab_postprocess_each)
batch_hijack.instance.postprocess_batch_callbacks.insert(0, self.batch_tab_postprocess)
def title(self):
return "ControlNet"
def show(self, is_img2img):
return scripts.AlwaysVisible
@staticmethod
def get_default_ui_unit(is_ui=True):
cls = UiControlNetUnit if is_ui else external_code.ControlNetUnit
return cls(
enabled=False,
module="none",
model="None"
)
def uigroup(self, tabname: str, is_img2img: bool, elem_id_tabname: str) -> Tuple[ControlNetUiGroup, gr.State]:
group = ControlNetUiGroup(
gradio_compat,
Script.get_default_ui_unit(),
self.preprocessor,
)
group.render(tabname, elem_id_tabname, is_img2img)
group.register_callbacks(is_img2img)
return group, group.render_and_register_unit(tabname, is_img2img)
def ui(self, is_img2img):
"""this function should create gradio UI elements. See https://gradio.app/docs/#components
The return value should be an array of all components that are used in processing.
Values of those returned components will be passed to run() and process() functions.
"""
infotext = Infotext()
controls = ()
max_models = shared.opts.data.get("control_net_unit_count", 3)
elem_id_tabname = ("img2img" if is_img2img else "txt2img") + "_controlnet"
with gr.Group(elem_id=elem_id_tabname):
with gr.Accordion(f"ControlNet {controlnet_version.version_flag}", open = False, elem_id="controlnet"):
if max_models > 1:
with gr.Tabs(elem_id=f"{elem_id_tabname}_tabs"):
for i in range(max_models):
with gr.Tab(f"ControlNet Unit {i}",
elem_classes=['cnet-unit-tab']):
group, state = self.uigroup(f"ControlNet-{i}", is_img2img, elem_id_tabname)
infotext.register_unit(i, group)
controls += (state,)
else:
with gr.Column():
group, state = self.uigroup(f"ControlNet", is_img2img, elem_id_tabname)
infotext.register_unit(0, group)
controls += (state,)
if shared.opts.data.get("control_net_sync_field_args", True):
self.infotext_fields = infotext.infotext_fields
self.paste_field_names = infotext.paste_field_names
return controls
@staticmethod
def clear_control_model_cache():
Script.model_cache.clear()
gc.collect()
devices.torch_gc()
@staticmethod
def load_control_model(p, unet, model):
if model in Script.model_cache:
logger.info(f"Loading model from cache: {model}")
return Script.model_cache[model]
# Remove model from cache to clear space before building another model
if len(Script.model_cache) > 0 and len(Script.model_cache) >= shared.opts.data.get("control_net_model_cache_size", 2):
Script.model_cache.popitem(last=False)
gc.collect()
devices.torch_gc()
model_net = Script.build_control_model(p, unet, model)
if shared.opts.data.get("control_net_model_cache_size", 2) > 0:
Script.model_cache[model] = model_net
return model_net
@staticmethod
def build_control_model(p, unet, model):
if model is None or model == 'None':
raise RuntimeError(f"You have not selected any ControlNet Model.")
model_path = global_state.cn_models.get(model, None)
if model_path is None:
model = find_closest_lora_model_name(model)
model_path = global_state.cn_models.get(model, None)
if model_path is None:
raise RuntimeError(f"model not found: {model}")
# trim '"' at start/end
if model_path.startswith("\"") and model_path.endswith("\""):
model_path = model_path[1:-1]
if not os.path.exists(model_path):
raise ValueError(f"file not found: {model_path}")
logger.info(f"Loading model: {model}")
state_dict = load_state_dict(model_path)
network = build_model_by_guess(state_dict, unet, model_path)
network.to('cpu', dtype=p.sd_model.dtype)
logger.info(f"ControlNet model {model} loaded.")
return network
@staticmethod
def get_remote_call(p, attribute, default=None, idx=0, strict=False, force=False):
if not force and not shared.opts.data.get("control_net_allow_script_control", False):
return default
def get_element(obj, strict=False):
if not isinstance(obj, list):
return obj if not strict or idx == 0 else None
elif idx < len(obj):
return obj[idx]
else:
return None
attribute_value = get_element(getattr(p, attribute, None), strict)
default_value = get_element(default)
return attribute_value if attribute_value is not None else default_value
@staticmethod
def parse_remote_call(p, unit: external_code.ControlNetUnit, idx):
selector = Script.get_remote_call
unit.enabled = selector(p, "control_net_enabled", unit.enabled, idx, strict=True)
unit.module = selector(p, "control_net_module", unit.module, idx)
unit.model = selector(p, "control_net_model", unit.model, idx)
unit.weight = selector(p, "control_net_weight", unit.weight, idx)
unit.image = selector(p, "control_net_image", unit.image, idx)
unit.resize_mode = selector(p, "control_net_resize_mode", unit.resize_mode, idx)
unit.low_vram = selector(p, "control_net_lowvram", unit.low_vram, idx)
unit.processor_res = selector(p, "control_net_pres", unit.processor_res, idx)
unit.threshold_a = selector(p, "control_net_pthr_a", unit.threshold_a, idx)
unit.threshold_b = selector(p, "control_net_pthr_b", unit.threshold_b, idx)
unit.guidance_start = selector(p, "control_net_guidance_start", unit.guidance_start, idx)
unit.guidance_end = selector(p, "control_net_guidance_end", unit.guidance_end, idx)
# Backward compatibility. See https://github.com/Mikubill/sd-webui-controlnet/issues/1740
# for more details.
unit.guidance_end = selector(p, "control_net_guidance_strength", unit.guidance_end, idx)
unit.control_mode = selector(p, "control_net_control_mode", unit.control_mode, idx)
unit.pixel_perfect = selector(p, "control_net_pixel_perfect", unit.pixel_perfect, idx)
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 get_pytorch_control(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 = 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
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
@staticmethod
def get_enabled_units(p):
units = external_code.get_all_units_in_processing(p)
if len(units) == 0:
# fill a null group
remote_unit = Script.parse_remote_call(p, Script.get_default_ui_unit(), 0)
if remote_unit.enabled:
units.append(remote_unit)
enabled_units = [
copy(local_unit)
for idx, unit in enumerate(units)
for local_unit in (Script.parse_remote_call(p, unit, idx),)
if local_unit.enabled
]
Infotext.write_infotext(enabled_units, p)
return enabled_units
@staticmethod
def choose_input_image(
p: processing.StableDiffusionProcessing,
unit: external_code.ControlNetUnit,
idx: int
) -> Tuple[np.ndarray, bool]:
""" Choose input image from following sources with descending priority:
- p.image_control: [Deprecated] Lagacy way to pass image to controlnet.
- p.control_net_input_image: [Deprecated] Lagacy way to pass image to controlnet.
- unit.image:
- ControlNet tab input image.
- Input image from API call.
- p.init_images: A1111 img2img tab input image.
Returns:
- The input image in ndarray form.
- Whether input image is from A1111.
"""
image_from_a1111 = False
p_input_image = Script.get_remote_call(p, "control_net_input_image", None, idx)
image = image_dict_from_any(unit.image)
if batch_hijack.instance.is_batch and getattr(p, "image_control", None) is not None:
logger.warning("Warn: Using legacy field 'p.image_control'.")
input_image = HWC3(np.asarray(p.image_control))
elif p_input_image is not None:
logger.warning("Warn: Using legacy field 'p.controlnet_input_image'")
if isinstance(p_input_image, dict) and "mask" in p_input_image and "image" in p_input_image:
color = HWC3(np.asarray(p_input_image['image']))
alpha = np.asarray(p_input_image['mask'])[..., None]
input_image = np.concatenate([color, alpha], axis=2)
else:
input_image = HWC3(np.asarray(p_input_image))
elif image is not None:
while len(image['mask'].shape) < 3:
image['mask'] = image['mask'][..., np.newaxis]
# Need to check the image for API compatibility
if isinstance(image['image'], str):
from modules.api.api import decode_base64_to_image
input_image = HWC3(np.asarray(decode_base64_to_image(image['image'])))
else:
input_image = HWC3(image['image'])
have_mask = 'mask' in image and not (
(image['mask'][:, :, 0] <= 5).all() or
(image['mask'][:, :, 0] >= 250).all()
)
if 'inpaint' in unit.module:
logger.info("using inpaint as input")
color = HWC3(image['image'])
if have_mask:
alpha = image['mask'][:, :, 0:1]
else:
alpha = np.zeros_like(color)[:, :, 0:1]
input_image = np.concatenate([color, alpha], axis=2)
else:
if have_mask and not shared.opts.data.get("controlnet_ignore_noninpaint_mask", False):
logger.info("using mask as input")
input_image = HWC3(image['mask'][:, :, 0])
unit.module = 'none' # Always use black bg and white line
else:
# use img2img init_image as default
input_image = getattr(p, "init_images", [None])[0]
if input_image is None:
if batch_hijack.instance.is_batch:
shared.state.interrupted = True
raise ValueError('controlnet is enabled but no input image is given')
input_image = HWC3(np.asarray(input_image))
image_from_a1111 = True
assert isinstance(input_image, np.ndarray)
return input_image, image_from_a1111
@staticmethod
def bound_check_params(unit: external_code.ControlNetUnit) -> None:
"""
Checks and corrects negative parameters in ControlNetUnit 'unit'.
Parameters 'processor_res', 'threshold_a', 'threshold_b' are reset to
their default values if negative.
Args:
unit (external_code.ControlNetUnit): The ControlNetUnit instance to check.
"""
cfg = preprocessor_sliders_config.get(
global_state.get_module_basename(unit.module), [])
defaults = {
param: cfg_default['value']
for param, cfg_default in zip(
("processor_res", 'threshold_a', 'threshold_b'), cfg)
if cfg_default is not None
}
for param, default_value in defaults.items():
value = getattr(unit, param)
if value < 0:
setattr(unit, param, default_value)
logger.warning(f'[{unit.module}.{param}] Invalid value({value}), using default value {default_value}.')
def controlnet_main_entry(self, p):
sd_ldm = p.sd_model
unet = sd_ldm.model.diffusion_model
self.noise_modifier = None
setattr(p, 'controlnet_control_loras', [])
if self.latest_network is not None:
# always restore (~0.05s)
self.latest_network.restore()
# always clear (~0.05s)
clear_all_secondary_control_models()
if not batch_hijack.instance.is_batch:
self.enabled_units = Script.get_enabled_units(p)
if len(self.enabled_units) == 0:
self.latest_network = None
return
detected_maps = []
forward_params = []
post_processors = []
# cache stuff
if self.latest_model_hash != p.sd_model.sd_model_hash:
Script.clear_control_model_cache()
for idx, unit in enumerate(self.enabled_units):
unit.module = global_state.get_module_basename(unit.module)
# unload unused preproc
module_list = [unit.module for unit in self.enabled_units]
for key in self.unloadable:
if key not in module_list:
self.unloadable.get(key, lambda:None)()
self.latest_model_hash = p.sd_model.sd_model_hash
for idx, unit in enumerate(self.enabled_units):
Script.bound_check_params(unit)
resize_mode = external_code.resize_mode_from_value(unit.resize_mode)
control_mode = external_code.control_mode_from_value(unit.control_mode)
if unit.module in model_free_preprocessors:
model_net = None
else:
model_net = Script.load_control_model(p, unet, unit.model)
model_net.reset()
if getattr(model_net, 'is_control_lora', False):
control_lora = model_net.control_model
bind_control_lora(unet, control_lora)
p.controlnet_control_loras.append(control_lora)
input_image, image_from_a1111 = Script.choose_input_image(p, unit, idx)
if image_from_a1111:
a1111_i2i_resize_mode = getattr(p, "resize_mode", None)
if a1111_i2i_resize_mode is not None:
resize_mode = external_code.resize_mode_from_value(a1111_i2i_resize_mode)
a1111_mask_image : Optional[Image.Image] = getattr(p, "image_mask", None)
if 'inpaint' in unit.module and not image_has_mask(input_image) and a1111_mask_image is not None:
a1111_mask = np.array(prepare_mask(a1111_mask_image, p))
if a1111_mask.ndim == 2:
if a1111_mask.shape[0] == input_image.shape[0]:
if a1111_mask.shape[1] == input_image.shape[1]:
input_image = np.concatenate([input_image[:, :, 0:3], a1111_mask[:, :, None]], axis=2)
a1111_i2i_resize_mode = getattr(p, "resize_mode", None)
if a1111_i2i_resize_mode is not None:
resize_mode = external_code.resize_mode_from_value(a1111_i2i_resize_mode)
if 'reference' not in unit.module and issubclass(type(p), StableDiffusionProcessingImg2Img) \
and p.inpaint_full_res and a1111_mask_image is not None:
logger.debug("A1111 inpaint mask START")
input_image = [input_image[:, :, i] for i in range(input_image.shape[2])]
input_image = [Image.fromarray(x) for x in input_image]
mask = prepare_mask(a1111_mask_image, p)
crop_region = masking.get_crop_region(np.array(mask), p.inpaint_full_res_padding)
crop_region = masking.expand_crop_region(crop_region, p.width, p.height, mask.width, mask.height)
input_image = [
images.resize_image(resize_mode.int_value(), i, mask.width, mask.height)
for i in input_image
]
input_image = [x.crop(crop_region) for x in input_image]
input_image = [
images.resize_image(external_code.ResizeMode.OUTER_FIT.int_value(), x, p.width, p.height)
for x in input_image
]
input_image = [np.asarray(x)[:, :, 0] for x in input_image]
input_image = np.stack(input_image, axis=2)
logger.debug("A1111 inpaint mask END")
if 'inpaint_only' == unit.module and issubclass(type(p), StableDiffusionProcessingImg2Img) and p.image_mask is not None:
logger.warning('A1111 inpaint and ControlNet inpaint duplicated. ControlNet support enabled.')
unit.module = 'inpaint'
# safe numpy
logger.debug("Safe numpy convertion START")
input_image = np.ascontiguousarray(input_image.copy()).copy()
logger.debug("Safe numpy convertion END")
logger.info(f"Loading preprocessor: {unit.module}")
preprocessor = self.preprocessor[unit.module]
high_res_fix = isinstance(p, StableDiffusionProcessingTxt2Img) and getattr(p, 'enable_hr', False)
h = (p.height // 8) * 8
w = (p.width // 8) * 8
if high_res_fix:
if p.hr_resize_x == 0 and p.hr_resize_y == 0:
hr_y = int(p.height * p.hr_scale)
hr_x = int(p.width * p.hr_scale)
else:
hr_y, hr_x = p.hr_resize_y, p.hr_resize_x
hr_y = (hr_y // 8) * 8
hr_x = (hr_x // 8) * 8
else:
hr_y = h
hr_x = w
if unit.module == 'inpaint_only+lama' and resize_mode == external_code.ResizeMode.OUTER_FIT:
# inpaint_only+lama is special and required outpaint fix
_, input_image = Script.detectmap_proc(input_image, unit.module, resize_mode, hr_y, hr_x)
control_model_type = ControlModelType.ControlNet
global_average_pooling = False
if 'reference' in unit.module:
control_model_type = ControlModelType.AttentionInjection
elif 'revision' in unit.module:
control_model_type = ControlModelType.ReVision
elif hasattr(model_net, 'control_model') and (isinstance(model_net.control_model, Adapter) or isinstance(model_net.control_model, Adapter_light)):
control_model_type = ControlModelType.T2I_Adapter
elif hasattr(model_net, 'control_model') and isinstance(model_net.control_model, StyleAdapter):
control_model_type = ControlModelType.T2I_StyleAdapter
elif isinstance(model_net, PlugableIPAdapter):
control_model_type = ControlModelType.IPAdapter
elif isinstance(model_net, PlugableControlLLLite):
control_model_type = ControlModelType.Controlllite
if control_model_type is ControlModelType.ControlNet:
global_average_pooling = model_net.control_model.global_average_pooling
preprocessor_resolution = unit.processor_res
if unit.pixel_perfect:
preprocessor_resolution = external_code.pixel_perfect_resolution(
input_image,
target_H=h,
target_W=w,
resize_mode=resize_mode
)
logger.info(f'preprocessor resolution = {preprocessor_resolution}')
# Preprocessor result may depend on numpy random operations, use the
# random seed in `StableDiffusionProcessing` to make the
# preprocessor result reproducable.
# Currently following preprocessors use numpy random:
# - shuffle
seed = set_numpy_seed(p)
logger.debug(f"Use numpy seed {seed}.")
detected_map, is_image = preprocessor(
input_image,
res=preprocessor_resolution,
thr_a=unit.threshold_a,
thr_b=unit.threshold_b,
)
if high_res_fix:
if is_image:
hr_control, hr_detected_map = Script.detectmap_proc(detected_map, unit.module, resize_mode, hr_y, hr_x)
detected_maps.append((hr_detected_map, unit.module))
else:
hr_control = detected_map
else:
hr_control = None
if is_image:
control, detected_map = Script.detectmap_proc(detected_map, unit.module, resize_mode, h, w)
detected_maps.append((detected_map, unit.module))
else:
control = detected_map
detected_maps.append((input_image, unit.module))
if control_model_type == ControlModelType.T2I_StyleAdapter:
control = control['last_hidden_state']
if control_model_type == ControlModelType.ReVision:
control = control['image_embeds']
preprocessor_dict = dict(
name=unit.module,
preprocessor_resolution=preprocessor_resolution,
threshold_a=unit.threshold_a,
threshold_b=unit.threshold_b
)
forward_param = ControlParams(
control_model=model_net,
preprocessor=preprocessor_dict,
hint_cond=control,
weight=unit.weight,
guidance_stopped=False,
start_guidance_percent=unit.guidance_start,
stop_guidance_percent=unit.guidance_end,
advanced_weighting=None,
control_model_type=control_model_type,
global_average_pooling=global_average_pooling,
hr_hint_cond=hr_control,
soft_injection=control_mode != external_code.ControlMode.BALANCED,
cfg_injection=control_mode == external_code.ControlMode.CONTROL,
)
forward_params.append(forward_param)
if 'inpaint_only' in unit.module:
final_inpaint_feed = hr_control if hr_control is not None else control
final_inpaint_feed = final_inpaint_feed.detach().cpu().numpy()
final_inpaint_feed = np.ascontiguousarray(final_inpaint_feed).copy()
final_inpaint_mask = final_inpaint_feed[0, 3, :, :].astype(np.float32)
final_inpaint_raw = final_inpaint_feed[0, :3].astype(np.float32)
sigma = shared.opts.data.get("control_net_inpaint_blur_sigma", 7)
final_inpaint_mask = cv2.dilate(final_inpaint_mask, np.ones((sigma, sigma), dtype=np.uint8))
final_inpaint_mask = cv2.blur(final_inpaint_mask, (sigma, sigma))[None]
_, Hmask, Wmask = final_inpaint_mask.shape
final_inpaint_raw = torch.from_numpy(np.ascontiguousarray(final_inpaint_raw).copy())
final_inpaint_mask = torch.from_numpy(np.ascontiguousarray(final_inpaint_mask).copy())
def inpaint_only_post_processing(x):
_, H, W = x.shape
if Hmask != H or Wmask != W:
logger.error('Error: ControlNet find post-processing resolution mismatch. This could be related to other extensions hacked processing.')
return x
r = final_inpaint_raw.to(x.dtype).to(x.device)
m = final_inpaint_mask.to(x.dtype).to(x.device)
y = m * x.clip(0, 1) + (1 - m) * r
y = y.clip(0, 1)
return y
post_processors.append(inpaint_only_post_processing)
if 'recolor' in unit.module:
final_feed = hr_control if hr_control is not None else control
final_feed = final_feed.detach().cpu().numpy()
final_feed = np.ascontiguousarray(final_feed).copy()
final_feed = final_feed[0, 0, :, :].astype(np.float32)
final_feed = (final_feed * 255).clip(0, 255).astype(np.uint8)
Hfeed, Wfeed = final_feed.shape
if 'luminance' in unit.module:
def recolor_luminance_post_processing(x):
C, H, W = x.shape
if Hfeed != H or Wfeed != W or C != 3:
logger.error('Error: ControlNet find post-processing resolution mismatch. This could be related to other extensions hacked processing.')
return x
h = x.detach().cpu().numpy().transpose((1, 2, 0))
h = (h * 255).clip(0, 255).astype(np.uint8)
h = cv2.cvtColor(h, cv2.COLOR_RGB2LAB)
h[:, :, 0] = final_feed
h = cv2.cvtColor(h, cv2.COLOR_LAB2RGB)
h = (h.astype(np.float32) / 255.0).transpose((2, 0, 1))
y = torch.from_numpy(h).clip(0, 1).to(x)
return y
post_processors.append(recolor_luminance_post_processing)
if 'intensity' in unit.module:
def recolor_intensity_post_processing(x):
C, H, W = x.shape
if Hfeed != H or Wfeed != W or C != 3:
logger.error('Error: ControlNet find post-processing resolution mismatch. This could be related to other extensions hacked processing.')
return x
h = x.detach().cpu().numpy().transpose((1, 2, 0))
h = (h * 255).clip(0, 255).astype(np.uint8)
h = cv2.cvtColor(h, cv2.COLOR_RGB2HSV)
h[:, :, 2] = final_feed
h = cv2.cvtColor(h, cv2.COLOR_HSV2RGB)
h = (h.astype(np.float32) / 255.0).transpose((2, 0, 1))
y = torch.from_numpy(h).clip(0, 1).to(x)
return y
post_processors.append(recolor_intensity_post_processing)
if '+lama' in unit.module:
forward_param.used_hint_cond_latent = hook.UnetHook.call_vae_using_process(p, control)
self.noise_modifier = forward_param.used_hint_cond_latent
del model_net
is_low_vram = any(unit.low_vram for unit in self.enabled_units)
self.latest_network = UnetHook(lowvram=is_low_vram)
self.latest_network.hook(model=unet, sd_ldm=sd_ldm, control_params=forward_params, process=p)
for param in forward_params:
if param.control_model_type == ControlModelType.IPAdapter:
param.control_model.hook(
model=unet,
clip_vision_output=param.hint_cond,
weight=param.weight,
dtype=torch.float32,
start=param.start_guidance_percent,
end=param.stop_guidance_percent
)
if param.control_model_type == ControlModelType.Controlllite:
param.control_model.hook(
model=unet,
cond=param.hint_cond,
weight=param.weight,
start=param.start_guidance_percent,
end=param.stop_guidance_percent
)
self.detected_map = detected_maps
self.post_processors = post_processors
def controlnet_hack(self, p):
t = time.time()
self.controlnet_main_entry(p)
if len(self.enabled_units) > 0:
logger.info(f'ControlNet Hooked - Time = {time.time() - t}')
return
@staticmethod
def process_has_sdxl_refiner(p):
return getattr(p, 'refiner_checkpoint', None) is not None
def process(self, p, *args, **kwargs):
if not self.process_has_sdxl_refiner(p):
self.controlnet_hack(p)
return
def before_process_batch(self, p, *args, **kwargs):
if self.noise_modifier is not None:
p.rng = HackedImageRNG(rng=p.rng,
noise_modifier=self.noise_modifier,
sd_model=p.sd_model)
self.noise_modifier = None
if self.process_has_sdxl_refiner(p):
self.controlnet_hack(p)
return
def postprocess_batch(self, p, *args, **kwargs):
images = kwargs.get('images', [])
for post_processor in self.post_processors:
for i in range(len(images)):
images[i] = post_processor(images[i])
return
def postprocess(self, p, processed, *args):
clear_all_secondary_control_models()
self.noise_modifier = None
for control_lora in getattr(p, 'controlnet_control_loras', []):
unbind_control_lora(control_lora)
p.controlnet_control_loras = []
self.post_processors = []
setattr(p, 'controlnet_vae_cache', None)
processor_params_flag = (', '.join(getattr(processed, 'extra_generation_params', []))).lower()
self.post_processors = []
if not batch_hijack.instance.is_batch:
self.enabled_units.clear()
if shared.opts.data.get("control_net_detectmap_autosaving", False) and self.latest_network is not None:
for detect_map, module in self.detected_map:
detectmap_dir = os.path.join(shared.opts.data.get("control_net_detectedmap_dir", ""), module)
if not os.path.isabs(detectmap_dir):
detectmap_dir = os.path.join(p.outpath_samples, detectmap_dir)
if module != "none":
os.makedirs(detectmap_dir, exist_ok=True)
img = Image.fromarray(np.ascontiguousarray(detect_map.clip(0, 255).astype(np.uint8)).copy())
save_image(img, detectmap_dir, module)
if self.latest_network is None:
return
if not batch_hijack.instance.is_batch:
if not shared.opts.data.get("control_net_no_detectmap", False):
if 'sd upscale' not in processor_params_flag:
if self.detected_map is not None:
for detect_map, module in self.detected_map:
if detect_map is None:
continue
detect_map = np.ascontiguousarray(detect_map.copy()).copy()
detect_map = external_code.visualize_inpaint_mask(detect_map)
processed.images.extend([
Image.fromarray(
detect_map.clip(0, 255).astype(np.uint8)
)
])
self.input_image = None
self.latest_network.restore()
self.latest_network = None
self.detected_map.clear()
gc.collect()
devices.torch_gc()
def batch_tab_process(self, p, batches, *args, **kwargs):
self.enabled_units = self.get_enabled_units(p)
for unit_i, unit in enumerate(self.enabled_units):
unit.batch_images = iter([batch[unit_i] for batch in batches])
def batch_tab_process_each(self, p, *args, **kwargs):
for unit_i, unit in enumerate(self.enabled_units):
if getattr(unit, 'loopback', False) and batch_hijack.instance.batch_index > 0: continue
unit.image = next(unit.batch_images)
def batch_tab_postprocess_each(self, p, processed, *args, **kwargs):
for unit_i, unit in enumerate(self.enabled_units):
if getattr(unit, 'loopback', False):
output_images = getattr(processed, 'images', [])[processed.index_of_first_image:]
if output_images:
unit.image = np.array(output_images[0])
else:
logger.warning(f'Warning: No loopback image found for controlnet unit {unit_i}. Using control map from last batch iteration instead')
def batch_tab_postprocess(self, p, *args, **kwargs):
self.enabled_units.clear()
self.input_image = None
if self.latest_network is None: return
self.latest_network.restore()
self.latest_network = None
self.detected_map.clear()
def on_ui_settings():
section = ('control_net', "ControlNet")
shared.opts.add_option("control_net_detectedmap_dir", shared.OptionInfo(
global_state.default_detectedmap_dir, "Directory for detected maps auto saving", section=section))
shared.opts.add_option("control_net_models_path", shared.OptionInfo(
"", "Extra path to scan for ControlNet models (e.g. training output directory)", section=section))
shared.opts.add_option("control_net_modules_path", shared.OptionInfo(
"", "Path to directory containing annotator model directories (requires restart, overrides corresponding command line flag)", section=section))
shared.opts.add_option("control_net_unit_count", shared.OptionInfo(
3, "Multi-ControlNet: ControlNet unit number (requires restart)", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}, section=section))
shared.opts.add_option("control_net_model_cache_size", shared.OptionInfo(
1, "Model cache size (requires restart)", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}, section=section))
shared.opts.add_option("control_net_inpaint_blur_sigma", shared.OptionInfo(
7, "ControlNet inpainting Gaussian blur sigma", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=section))
shared.opts.add_option("control_net_no_high_res_fix", shared.OptionInfo(
False, "Do not apply ControlNet during highres fix", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("control_net_no_detectmap", shared.OptionInfo(
False, "Do not append detectmap to output", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("control_net_detectmap_autosaving", shared.OptionInfo(
False, "Allow detectmap auto saving", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("control_net_allow_script_control", shared.OptionInfo(
False, "Allow other script to control this extension", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("control_net_sync_field_args", shared.OptionInfo(
True, "Paste ControlNet parameters in infotext", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("controlnet_show_batch_images_in_ui", shared.OptionInfo(
False, "Show batch images in gradio gallery output", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("controlnet_increment_seed_during_batch", shared.OptionInfo(
False, "Increment seed after each controlnet batch iteration", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("controlnet_disable_control_type", shared.OptionInfo(
False, "Disable control type selection", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("controlnet_disable_openpose_edit", shared.OptionInfo(
False, "Disable openpose edit", gr.Checkbox, {"interactive": True}, section=section))
shared.opts.add_option("controlnet_ignore_noninpaint_mask", shared.OptionInfo(
False, "Ignore mask on ControlNet input image if control type is not inpaint",
gr.Checkbox, {"interactive": True}, section=section))
batch_hijack.instance.do_hijack()
script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_infotext_pasted(Infotext.on_infotext_pasted)
script_callbacks.on_after_component(ControlNetUiGroup.on_after_component)
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