import gc import os from collections import OrderedDict from copy import copy from typing import Dict, Optional import importlib import modules.scripts as scripts from modules import shared, devices, script_callbacks, processing, masking, images import gradio as gr from einops import rearrange from scripts import global_state, hook, external_code, processor, batch_hijack, controlnet_version, utils importlib.reload(processor) importlib.reload(utils) importlib.reload(global_state) importlib.reload(hook) importlib.reload(external_code) importlib.reload(batch_hijack) from scripts.cldm import PlugableControlModel from scripts.processor import * from scripts.adapter import PlugableAdapter from scripts.utils import load_state_dict from scripts.hook import ControlParams, UnetHook, ControlModelType from scripts.ui.controlnet_ui_group import ControlNetUiGroup, UiControlNetUnit from modules.processing import StableDiffusionProcessingImg2Img, StableDiffusionProcessingTxt2Img from modules.images import save_image 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 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 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 class Script(scripts.Script): 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 = [] 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 def get_threshold_block(self, proc): pass def get_default_ui_unit(self, 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): group = ControlNetUiGroup( gradio_compat, self.infotext_fields, self.get_default_ui_unit(), self.preprocessor, ) group.render(tabname, elem_id_tabname) group.register_callbacks(is_img2img) return 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. """ self.infotext_fields = [] self.paste_field_names = [] controls = () max_models = shared.opts.data.get("control_net_max_models_num", 1) 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}"): controls += (self.uigroup(f"ControlNet-{i}", is_img2img, elem_id_tabname),) else: with gr.Column(): controls += (self.uigroup(f"ControlNet", is_img2img, elem_id_tabname),) if shared.opts.data.get("control_net_sync_field_args", False): for _, field_name in self.infotext_fields: self.paste_field_names.append(field_name) return controls def clear_control_model_cache(self): Script.model_cache.clear() gc.collect() devices.torch_gc() def load_control_model(self, p, unet, model, lowvram): if model in Script.model_cache: print(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 = self.build_control_model(p, unet, model, lowvram) if shared.opts.data.get("control_net_model_cache_size", 2) > 0: Script.model_cache[model] = model_net return model_net def build_control_model(self, p, unet, model, lowvram): 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}") print(f"Loading model: {model}") state_dict = load_state_dict(model_path) network_module = PlugableControlModel network_config = shared.opts.data.get("control_net_model_config", global_state.default_conf) if not os.path.isabs(network_config): network_config = os.path.join(global_state.script_dir, network_config) if any([k.startswith("body.") or k == 'style_embedding' for k, v in state_dict.items()]): # adapter model network_module = PlugableAdapter network_config = shared.opts.data.get("control_net_model_adapter_config", global_state.default_conf_adapter) if not os.path.isabs(network_config): network_config = os.path.join(global_state.script_dir, network_config) model_path = os.path.abspath(model_path) model_stem = Path(model_path).stem model_dir_name = os.path.dirname(model_path) possible_config_filenames = [ os.path.join(model_dir_name, model_stem + ".yaml"), os.path.join(global_state.script_dir, 'models', model_stem + ".yaml"), os.path.join(model_dir_name, model_stem.replace('_fp16', '') + ".yaml"), os.path.join(global_state.script_dir, 'models', model_stem.replace('_fp16', '') + ".yaml"), os.path.join(model_dir_name, model_stem.replace('_diff', '') + ".yaml"), os.path.join(global_state.script_dir, 'models', model_stem.replace('_diff', '') + ".yaml"), os.path.join(model_dir_name, model_stem.replace('-fp16', '') + ".yaml"), os.path.join(global_state.script_dir, 'models', model_stem.replace('-fp16', '') + ".yaml"), os.path.join(model_dir_name, model_stem.replace('-diff', '') + ".yaml"), os.path.join(global_state.script_dir, 'models', model_stem.replace('-diff', '') + ".yaml") ] override_config = possible_config_filenames[0] for possible_config_filename in possible_config_filenames: if os.path.exists(possible_config_filename): override_config = possible_config_filename break if 'v11' in model_stem.lower() or 'shuffle' in model_stem.lower(): assert os.path.exists(override_config), f'Error: The model config {override_config} is missing. ControlNet 1.1 must have configs.' if os.path.exists(override_config): network_config = override_config else: print(f'ERROR: ControlNet cannot find model config [{override_config}] \n' f'ERROR: ControlNet will use a WRONG config [{network_config}] to load your model. \n' f'ERROR: The WRONG config may not match your model. The generated results can be bad. \n' f'ERROR: You are using a ControlNet model [{model_stem}] without correct YAML config file. \n' f'ERROR: The performance of this model may be worse than your expectation. \n' f'ERROR: If this model cannot get good results, the reason is that you do not have a YAML file for the model. \n' f'Solution: Please download YAML file, or ask your model provider to provide [{override_config}] for you to download.\n' f'Hint: You can take a look at [{os.path.join(global_state.script_dir, "models")}] to find many existing YAML files.\n') print(f"Loading config: {network_config}") network = network_module( state_dict=state_dict, config_path=network_config, lowvram=lowvram, base_model=unet, ) network.to(p.sd_model.device, dtype=p.sd_model.dtype) print(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 def parse_remote_call(self, p, unit: external_code.ControlNetUnit, idx): selector = self.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) 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 def detectmap_proc(self, 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] 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 = np.unique(x.reshape(-1, x.shape[2]), axis=0).shape[0] 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) 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): units = external_code.get_all_units_in_processing(p) enabled_units = [] if len(units) == 0: # fill a null group remote_unit = self.parse_remote_call(p, self.get_default_ui_unit(), 0) if remote_unit.enabled: units.append(remote_unit) for idx, unit in enumerate(units): unit = self.parse_remote_call(p, unit, idx) if not unit.enabled: continue enabled_units.append(copy(unit)) if len(units) != 1: log_key = f"ControlNet {idx}" else: log_key = "ControlNet" log_value = { "preprocessor": unit.module, "model": unit.model, "weight": unit.weight, "starting/ending": str((unit.guidance_start, unit.guidance_end)), "resize mode": str(unit.resize_mode), "pixel perfect": str(unit.pixel_perfect), "control mode": str(unit.control_mode), "preprocessor params": str((unit.processor_res, unit.threshold_a, unit.threshold_b)), } log_value = str(log_value).replace('\'', '').replace('{', '').replace('}', '') p.extra_generation_params.update({log_key: log_value}) return enabled_units def process(self, p, *args): """ This function is called before processing begins for AlwaysVisible scripts. You can modify the processing object (p) here, inject hooks, etc. args contains all values returned by components from ui() """ sd_ldm = p.sd_model unet = sd_ldm.model.diffusion_model if self.latest_network is not None: # always restore (~0.05s) self.latest_network.restore(unet) if not batch_hijack.instance.is_batch: self.enabled_units = self.get_enabled_units(p) if len(self.enabled_units) == 0: self.latest_network = None return detected_maps = [] forward_params = [] post_processors = [] hook_lowvram = False # cache stuff if self.latest_model_hash != p.sd_model.sd_model_hash: self.clear_control_model_cache() # 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): unit.module = global_state.get_module_basename(unit.module) p_input_image = self.get_remote_call(p, "control_net_input_image", None, idx) image = image_dict_from_any(unit.image) if image is not None: while len(image['mask'].shape) < 3: image['mask'] = image['mask'][..., np.newaxis] resize_mode = external_code.resize_mode_from_value(unit.resize_mode) control_mode = external_code.control_mode_from_value(unit.control_mode) if unit.low_vram: hook_lowvram = True if unit.module in model_free_preprocessors: model_net = None else: model_net = self.load_control_model(p, unet, unit.model, unit.low_vram) model_net.reset() if batch_hijack.instance.is_batch and getattr(p, "image_control", None) is not None: input_image = HWC3(np.asarray(p.image_control)) elif p_input_image is not None: 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: # 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] == 0).all() or (image['mask'][:, :, 0] == 255).all()) if 'inpaint' in unit.module: print("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: print("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)) a1111_i2i_resize_mode = getattr(p, "resize_mode", None) if a1111_i2i_resize_mode is not None: if a1111_i2i_resize_mode == 0: resize_mode = external_code.ResizeMode.RESIZE elif a1111_i2i_resize_mode == 1: resize_mode = external_code.ResizeMode.INNER_FIT elif a1111_i2i_resize_mode == 2: resize_mode = external_code.ResizeMode.OUTER_FIT has_mask = False if input_image.ndim == 3: if input_image.shape[2] == 4: if np.max(input_image[:, :, 3]) > 127: has_mask = True a1111_mask = getattr(p, "image_mask", None) if 'inpaint' in unit.module and not has_mask and a1111_mask is not None: a1111_mask = a1111_mask.convert('L') if getattr(p, "inpainting_mask_invert", False): a1111_mask = ImageOps.invert(a1111_mask) if getattr(p, "mask_blur", 0) > 0: a1111_mask = a1111_mask.filter(ImageFilter.GaussianBlur(p.mask_blur)) a1111_mask = np.asarray(a1111_mask) 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) input_image = np.ascontiguousarray(input_image.copy()).copy() a1111_i2i_resize_mode = getattr(p, "resize_mode", None) if a1111_i2i_resize_mode is not None: if a1111_i2i_resize_mode == 0: resize_mode = external_code.ResizeMode.RESIZE elif a1111_i2i_resize_mode == 1: resize_mode = external_code.ResizeMode.INNER_FIT elif a1111_i2i_resize_mode == 2: resize_mode = external_code.ResizeMode.OUTER_FIT if 'reference' not in unit.module and issubclass(type(p), StableDiffusionProcessingImg2Img) \ and p.inpaint_full_res and p.image_mask is not None: input_image = [input_image[:, :, i] for i in range(input_image.shape[2])] input_image = [Image.fromarray(x) for x in input_image] mask = p.image_mask.convert('L') if p.inpainting_mask_invert: mask = ImageOps.invert(mask) if p.mask_blur > 0: mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur)) 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) if resize_mode == external_code.ResizeMode.INNER_FIT: input_image = [images.resize_image(1, i, mask.width, mask.height) for i in input_image] elif resize_mode == external_code.ResizeMode.OUTER_FIT: input_image = [images.resize_image(2, i, mask.width, mask.height) for i in input_image] else: input_image = [images.resize_image(0, 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(2, 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) if 'inpaint' in unit.module and issubclass(type(p), StableDiffusionProcessingImg2Img) \ and p.inpainting_fill and p.image_mask is not None: print('A1111 inpaint and ControlNet inpaint duplicated. ControlNet support enabled.') unit.module = 'inpaint' 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)) np.random.seed((tmp_seed + tmp_subseed) & 0xFFFFFFFF) except Exception as e: print(e) print('Warning: Failed to use consistent random seed.') # safe numpy input_image = np.ascontiguousarray(input_image.copy()).copy() print(f"Loading preprocessor: {unit.module}") preprocessor = self.preprocessor[unit.module] h, w, bsz = p.height, p.width, p.batch_size h = (h // 8) * 8 w = (w // 8) * 8 preprocessor_resolution = unit.processor_res if unit.pixel_perfect: raw_H, raw_W, _ = input_image.shape target_H, target_W = h, w k0 = float(target_H) / float(raw_H) k1 = float(target_W) / float(raw_W) if resize_mode == external_code.ResizeMode.OUTER_FIT: estimation = min(k0, k1) * float(min(raw_H, raw_W)) else: estimation = max(k0, k1) * float(min(raw_H, raw_W)) preprocessor_resolution = int(np.round(estimation)) print(f'Pixel Perfect Mode Enabled.') print(f'resize_mode = {str(resize_mode)}') print(f'raw_H = {raw_H}') print(f'raw_W = {raw_W}') print(f'target_H = {target_H}') print(f'target_W = {target_W}') print(f'estimation = {estimation}') print(f'preprocessor resolution = {preprocessor_resolution}') detected_map, is_image = preprocessor(input_image, res=preprocessor_resolution, thr_a=unit.threshold_a, thr_b=unit.threshold_b) if unit.module == "none" and "style" in unit.model: detected_map_bytes = detected_map[:,:,0].tobytes() detected_map = np.ndarray((round(input_image.shape[0]/4),input_image.shape[1]),dtype="float32",buffer=detected_map_bytes) detected_map = torch.Tensor(detected_map).to(devices.get_device_for("controlnet")) is_image = False if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr: 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 if is_image: hr_control, hr_detected_map = self.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 = self.detectmap_proc(detected_map, unit.module, resize_mode, h, w) detected_maps.append((detected_map, unit.module)) else: control = detected_map if unit.module == 'clip_vision': detected_maps.append((processor.clip_vision_visualization(detected_map), unit.module)) control_model_type = ControlModelType.ControlNet if isinstance(model_net, PlugableAdapter): control_model_type = ControlModelType.T2I_Adapter if getattr(model_net, "target", None) == "scripts.adapter.StyleAdapter": control_model_type = ControlModelType.T2I_StyleAdapter if 'reference' in unit.module: control_model_type = ControlModelType.AttentionInjection global_average_pooling = False if model_net is not None: if model_net.config.model.params.get("global_average_pooling", False): global_average_pooling = True 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 unit.module == 'inpaint_only': 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 = 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: print('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) return m * x + (1 - m) * r post_processors.append(inpaint_only_post_processing) del model_net self.latest_network = UnetHook(lowvram=hook_lowvram) self.latest_network.hook(model=unet, sd_ldm=sd_ldm, control_params=forward_params, process=p) self.detected_map = detected_maps self.post_processors = post_processors def postprocess_batch(self, p, *args, **kwargs): images = kwargs.get('images', []) for post_processor in self.post_processors: for i in range(images.shape[0]): images[i] = post_processor(images[i]) return def postprocess(self, p, processed, *args): processor_params_flag = (', '.join(getattr(processed, 'extra_generation_params', []))).lower() 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() if detect_map.ndim == 3 and detect_map.shape[2] == 4: inpaint_mask = detect_map[:, :, 3] detect_map = detect_map[:, :, 0:3] detect_map[inpaint_mask > 127] = 0 processed.images.extend([ Image.fromarray( detect_map.clip(0, 255).astype(np.uint8) ) ]) self.input_image = None self.latest_network.restore(p.sd_model.model.diffusion_model) 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: print(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(shared.sd_model.model.diffusion_model) self.latest_network = None self.detected_map.clear() def on_ui_settings(): section = ('control_net', "ControlNet") shared.opts.add_option("control_net_model_config", shared.OptionInfo( global_state.default_conf, "Config file for Control Net models", section=section)) shared.opts.add_option("control_net_model_adapter_config", shared.OptionInfo( global_state.default_conf_adapter, "Config file for Adapter models", section=section)) 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_max_models_num", shared.OptionInfo( 1, "Multi ControlNet: Max models amount (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_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( False, "Passing ControlNet parameters with \"Send to img2img\"", 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)) batch_hijack.instance.do_hijack() script_callbacks.on_ui_settings(on_ui_settings) script_callbacks.on_after_component(ControlNetUiGroup.on_after_component)