import os.path import stat import functools from collections import OrderedDict from modules import shared, scripts, sd_models from modules.paths import models_path from scripts.processor import * from scripts.utils import ndarray_lru_cache from typing import Dict, Callable, Optional CN_MODEL_EXTS = [".pt", ".pth", ".ckpt", ".safetensors"] cn_models_dir = os.path.join(models_path, "ControlNet") cn_models_dir_old = os.path.join(scripts.basedir(), "models") cn_models = OrderedDict() # "My_Lora(abcd1234)" -> C:/path/to/model.safetensors cn_models_names = {} # "my_lora" -> "My_Lora(abcd1234)" def cache_preprocessors(preprocessor_modules: Dict[str, Callable]) -> Dict[str, Callable]: """ We want to share the preprocessor results in a single big cache, instead of a small cache for each preprocessor function. """ CACHE_SIZE = shared.cmd_opts.controlnet_preprocessor_cache_size # Set CACHE_SIZE = 0 will completely remove the caching layer. This can be # helpful when debugging preprocessor code. if CACHE_SIZE == 0: return preprocessor_modules print(f'Create LRU cache (max_size={CACHE_SIZE}) for preprocessor results.') @ndarray_lru_cache(max_size=CACHE_SIZE) def unified_preprocessor(preprocessor_name: str, *args, **kwargs): # TODO: Make this a debug log? print(f'Calling preprocessor {preprocessor_name} outside of cache.') return preprocessor_modules[preprocessor_name](*args, **kwargs) # TODO: Introduce a seed parameter for shuffle preprocessor? uncacheable_preprocessors = ['shuffle'] return { k: ( v if k in uncacheable_preprocessors else functools.partial(unified_preprocessor, k) ) for k, v in preprocessor_modules.items() } cn_preprocessor_modules = { "none": lambda x, *args, **kwargs: (x, True), "canny": canny, "depth": midas, "depth_leres": functools.partial(leres, boost=False), "depth_leres++": functools.partial(leres, boost=True), "hed": hed, "hed_safe": hed_safe, "mediapipe_face": mediapipe_face, "mlsd": mlsd, "normal_map": midas_normal, "openpose": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=False, include_face=False), "openpose_hand": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=True, include_face=False), "openpose_face": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=False, include_face=True), "openpose_faceonly": functools.partial(g_openpose_model.run_model, include_body=False, include_hand=False, include_face=True), "openpose_full": functools.partial(g_openpose_model.run_model, include_body=True, include_hand=True, include_face=True), "clip_vision": clip, "color": color, "pidinet": pidinet, "pidinet_safe": pidinet_safe, "pidinet_sketch": pidinet_ts, "pidinet_scribble": scribble_pidinet, "scribble_xdog": scribble_xdog, "scribble_hed": scribble_hed, "segmentation": uniformer, "threshold": threshold, "depth_zoe": zoe_depth, "normal_bae": normal_bae, "oneformer_coco": oneformer_coco, "oneformer_ade20k": oneformer_ade20k, "lineart": lineart, "lineart_coarse": lineart_coarse, "lineart_anime": lineart_anime, "lineart_standard": lineart_standard, "shuffle": shuffle, "tile_resample": tile_resample, "invert": invert, "lineart_anime_denoise": lineart_anime_denoise, "reference_only": identity, "reference_adain": identity, "reference_adain+attn": identity, "inpaint": identity, "inpaint_only": identity, "tile_colorfix": identity, "tile_colorfix+sharp": identity, } cn_preprocessor_unloadable = { "hed": unload_hed, "fake_scribble": unload_hed, "mlsd": unload_mlsd, "clip": unload_clip, "depth": unload_midas, "depth_leres": unload_leres, "normal_map": unload_midas, "pidinet": unload_pidinet, "openpose": g_openpose_model.unload, "openpose_hand": g_openpose_model.unload, "openpose_face": g_openpose_model.unload, "openpose_full": g_openpose_model.unload, "segmentation": unload_uniformer, "depth_zoe": unload_zoe_depth, "normal_bae": unload_normal_bae, "oneformer_coco": unload_oneformer_coco, "oneformer_ade20k": unload_oneformer_ade20k, "lineart": unload_lineart, "lineart_coarse": unload_lineart_coarse, "lineart_anime": unload_lineart_anime, "lineart_anime_denoise": unload_lineart_anime_denoise } preprocessor_aliases = { "invert": "invert (from white bg & black line)", "lineart_standard": "lineart_standard (from white bg & black line)", "lineart": "lineart_realistic", "color": "t2ia_color_grid", "clip_vision": "t2ia_style_clipvision", "pidinet_sketch": "t2ia_sketch_pidi", "depth": "depth_midas", "normal_map": "normal_midas", "hed": "softedge_hed", "hed_safe": "softedge_hedsafe", "pidinet": "softedge_pidinet", "pidinet_safe": "softedge_pidisafe", "segmentation": "seg_ufade20k", "oneformer_coco": "seg_ofcoco", "oneformer_ade20k": "seg_ofade20k", "pidinet_scribble": "scribble_pidinet", "inpaint": "inpaint_global_harmonious", } ui_preprocessor_keys = ['none', preprocessor_aliases['invert']] ui_preprocessor_keys += sorted([preprocessor_aliases.get(k, k) for k in cn_preprocessor_modules.keys() if preprocessor_aliases.get(k, k) not in ui_preprocessor_keys]) reverse_preprocessor_aliases = {preprocessor_aliases[k]: k for k in preprocessor_aliases.keys()} def get_module_basename(module: Optional[str]) -> str: if module is None: module = 'none' return reverse_preprocessor_aliases.get(module, module) default_conf = os.path.join("models", "cldm_v15.yaml") default_conf_adapter = os.path.join("models", "t2iadapter_sketch_sd14v1.yaml") cn_detectedmap_dir = os.path.join("detected_maps") default_detectedmap_dir = cn_detectedmap_dir script_dir = scripts.basedir() os.makedirs(cn_models_dir, exist_ok=True) os.makedirs(cn_detectedmap_dir, exist_ok=True) def traverse_all_files(curr_path, model_list): f_list = [(os.path.join(curr_path, entry.name), entry.stat()) for entry in os.scandir(curr_path)] for f_info in f_list: fname, fstat = f_info if os.path.splitext(fname)[1] in CN_MODEL_EXTS: model_list.append(f_info) elif stat.S_ISDIR(fstat.st_mode): model_list = traverse_all_files(fname, model_list) return model_list def get_all_models(sort_by, filter_by, path): res = OrderedDict() fileinfos = traverse_all_files(path, []) filter_by = filter_by.strip(" ") if len(filter_by) != 0: fileinfos = [x for x in fileinfos if filter_by.lower() in os.path.basename(x[0]).lower()] if sort_by == "name": fileinfos = sorted(fileinfos, key=lambda x: os.path.basename(x[0])) elif sort_by == "date": fileinfos = sorted(fileinfos, key=lambda x: -x[1].st_mtime) elif sort_by == "path name": fileinfos = sorted(fileinfos) for finfo in fileinfos: filename = finfo[0] name = os.path.splitext(os.path.basename(filename))[0] # Prevent a hypothetical "None.pt" from being listed. if name != "None": res[name + f" [{sd_models.model_hash(filename)}]"] = filename return res def update_cn_models(): cn_models.clear() ext_dirs = (shared.opts.data.get("control_net_models_path", None), getattr(shared.cmd_opts, 'controlnet_dir', None)) extra_lora_paths = (extra_lora_path for extra_lora_path in ext_dirs if extra_lora_path is not None and os.path.exists(extra_lora_path)) paths = [cn_models_dir, cn_models_dir_old, *extra_lora_paths] for path in paths: sort_by = shared.opts.data.get( "control_net_models_sort_models_by", "name") filter_by = shared.opts.data.get("control_net_models_name_filter", "") found = get_all_models(sort_by, filter_by, path) cn_models.update({**found, **cn_models}) # insert "None" at the beginning of `cn_models` in-place cn_models_copy = OrderedDict(cn_models) cn_models.clear() cn_models.update({**{"None": None}, **cn_models_copy}) cn_models_names.clear() for name_and_hash, filename in cn_models.items(): if filename is None: continue name = os.path.splitext(os.path.basename(filename))[0].lower() cn_models_names[name] = name_and_hash